Meta Senior Data Engineer Interview Preparation Guide

Data Engineer

Interview Rounds

Recruiter Screening

30 min5 focus topicsbehavioral

What to Expect

Initial call with a Meta recruiter to assess cultural fit, motivation, and basic technical background. This 30-minute conversation covers your professional background, interest in Meta, data engineering experience, and high-level technical competencies. The recruiter evaluates your communication skills, enthusiasm for the role, and general suitability for the interview loop. Expect questions about your career progression, why you're interested in data engineering, why Meta specifically, and what aspects of the role appeal to you.

Tips & Advice

Be genuine and specific about why Meta excites you. Go beyond generic answers—mention specific Meta products and discuss the data engineering challenges behind them. Highlight 1-2 relevant projects from your background that showcase depth in data infrastructure or pipeline design. Keep technical discussion at a high level; save deep dives for technical rounds. Show enthusiasm and ask thoughtful questions about the team and role. Prepare a concise 2-3 minute introduction covering: current role, 1-2 key accomplishments in data engineering, and why you're seeking this opportunity.

Focus Topics

Communication Style & Cultural Fit

Demonstrate clear, thoughtful communication. Explain technical concepts concisely without jargon unless necessary. Show intellectual humility, curiosity about different perspectives, and openness to feedback. Meta values collaborative engineers who can work effectively across teams (data scientists, product managers, infrastructure teams, etc.).

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Motivation for Data Engineering

Clearly articulate why you're passionate about data engineering specifically. Discuss what excites you about building data infrastructure, ETL systems, and enabling data-driven decision-making at scale. Connect your motivation to Meta's mission and the specific challenges of handling petabyte-scale data systems.

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Why Meta & Product Knowledge

Demonstrate genuine knowledge of Meta's products (Facebook, Instagram, WhatsApp, Reels, Ads Manager) and articulate specific reasons for wanting to work on data infrastructure at Meta. Discuss scale challenges (billions of users), technical problems you find intellectually stimulating, or specific Meta initiatives that excite you. Show you understand the data engineering requirements of social platforms at Meta's scale.

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Key Project & Technical Achievements

Prepare 2-3 specific examples of significant data engineering projects you've led or significantly contributed to. For each, be ready to discuss: the business problem, your technical solution, technologies used (Spark, Airflow, cloud platforms, etc.), scale of data handled, and measurable impact. Focus on projects demonstrating pipeline design, system architecture, or data infrastructure ownership.

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Professional Background & Career Progression

Articulate your career journey as a data engineer with 5-12 years of experience. Focus on progression from junior/mid-level to senior responsibilities, demonstrating increasing ownership of complex projects, mentorship of team members, and influence on data architecture decisions. Highlight how you've grown from implementing pipelines to designing scalable data infrastructure.

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Technical Phone Screen - SQL

50 min5 focus topicstechnical

What to Expect

First technical screening call focusing on SQL expertise and analytical thinking. In this 45-50 minute interview, you'll be asked to write SQL queries to solve business problems using shared coding platforms (like CoderPad or similar). Expect 3-4 medium to hard SQL problems that progress in difficulty. Questions typically involve complex joins, window functions, CTEs, aggregations, and data manipulation. You'll need to write correct, optimized SQL and explain your approach. Interviewers assess SQL proficiency, analytical thinking, ability to handle edge cases, and problem-solving approach under time pressure.

Tips & Advice

Start by clarifying requirements and thinking aloud about your approach before coding. Ask questions about edge cases, data volume, and expected output format. Write clean, readable SQL with proper formatting. Optimize for readability first, then performance. Test your queries mentally against edge cases (NULLs, duplicates, zero records). If you get stuck, communicate your thinking and ask for hints—interviewers appreciate your problem-solving process over perfect solutions. Practice writing SQL on actual platforms to get comfortable. For senior level, expect questions combining multiple concepts (window functions + CTEs + joins). Be prepared to explain query execution plans and suggest indexes for optimization.

Focus Topics

Query Optimization & Performance

Understand basic query execution plans and how databases optimize queries. Know about indexing strategies (B-tree indexes, composite indexes). Recognize inefficient patterns (SELECT * is often suboptimal, cartesian products, unnecessary subqueries). Discuss trade-offs between different query approaches and justify your optimization choices.

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Common Table Expressions (CTEs) & Recursive Queries

Write multi-step CTEs (WITH clauses) to break complex problems into manageable steps. Understand recursive CTEs for hierarchical data or iterative problems. Use CTEs for readability and to avoid nested subqueries. For senior level, handle complex recursive scenarios.

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Advanced SQL Queries & Window Functions

Master window functions (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, SUM OVER, AVG OVER, etc.) for analytical queries. Understand ranking, running totals, and comparing current row with previous/next rows. Be comfortable using PARTITION BY and ORDER BY clauses. For senior level, combine window functions with CTEs and complex joins to solve multi-step analytical problems.

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NULL Handling & Edge Cases

Understand NULL semantics in SQL. Use COALESCE, NULLIF, and IS NULL / IS NOT NULL appropriately. Handle edge cases like division by zero, empty result sets, and queries returning different data types. Write robust queries that don't fail or produce incorrect results for unexpected data.

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Complex Joins & Multi-Table Aggregations

Be proficient with INNER, LEFT, RIGHT, FULL OUTER joins and self-joins. Understand how to join multiple tables and aggregate across them correctly. Handle scenarios with duplicate rows, multiple matches, and many-to-many relationships. Know when to use joins vs subqueries and understand performance implications.

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Technical Phone Screen - Python & Coding

50 min5 focus topicstechnical

What to Expect

Second technical screening call focusing on Python programming and data processing logic. In this 45-50 minute interview, you'll solve 3-4 algorithmic problems using Python on a shared coding platform. Problems range from medium to hard difficulty and often involve data structures (lists, dictionaries, sets), string manipulation, algorithms (sorting, searching, graph traversal), and time/space complexity analysis. Expect problems like finding patterns in data, processing sequences, or simulating data transformations. Interviewers assess coding ability, problem-solving approach, code quality, and ability to think about complexity and edge cases.

Tips & Advice

Think out loud and explain your approach before coding. Ask clarifying questions about input constraints and expected complexity. Start with a correct solution, then optimize if needed. Write clean, readable code with meaningful variable names. Handle edge cases (empty input, single element, duplicates). Discuss time and space complexity explicitly. For senior level, you're expected to write near-perfect code with minimal bugs. Test your solution mentally with examples. If you get stuck, explain your thinking and ask clarifying questions. Be comfortable explaining why you chose a particular data structure or algorithm.

Focus Topics

String Manipulation & Pattern Matching

Work with string operations, searching, splitting, joining, and pattern matching. Understand string immutability and efficient string building. Handle character encoding and case sensitivity. Solve problems like finding mismatched words, checking substrings, and data parsing.

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Edge Cases & Error Handling

Anticipate edge cases: empty input, single element, duplicates, invalid input, and boundary conditions. Write code that handles these gracefully. Understand when to raise exceptions vs return special values. For data processing, handle cases like missing data, type mismatches, and malformed records.

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Algorithm Design & Complexity Analysis

Solve problems using appropriate algorithms: searching (binary search), sorting, recursion, and dynamic programming when applicable. Understand Big O notation and analyze time/space complexity. Choose algorithms with optimal complexity and justify your choices. For hard problems, optimize from initial O(n²) to O(n log n) or O(n) solutions.

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Python Data Structures & Collections

Master Python's built-in data structures: lists, tuples, dictionaries, sets, and deques. Understand their performance characteristics (O(1) vs O(n) operations). Know when to use each: dictionaries for fast lookups, sets for membership testing and deduplication, deques for efficient queue/stack operations. Be comfortable with nested structures and understand immutability implications.

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List Manipulation & Data Transformation

Solve problems involving list operations: finding elements, sorting, filtering, transforming, and computing aggregates. Handle problems like finding monotonic sequences, computing running averages, or extracting specific patterns. Understand list comprehensions and functional approaches for efficient data transformation.

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Onsite Round 1 - Data Modeling & Schema Design

60 min5 focus topicssystem design

What to Expect

First onsite round focusing on data modeling and schema design for analytics. In this 50-60 minute interview, you'll design data models and schemas to support specific analytics use cases. Expect questions like 'Design a data model for tracking Facebook post interactions' or 'Create a schema for an e-commerce platform'. You'll discuss dimensional modeling (star schema, snowflake schema), fact and dimension tables, normalization vs denormalization trade-offs, partitioning strategies, and query optimization. Expect to draw diagrams, discuss join patterns, and explain how your design supports specific queries. Interviewers assess your data architecture thinking, understanding of analytics patterns, and ability to balance query performance with storage efficiency.

Tips & Advice

Start by asking clarifying questions: What queries need to be supported? What's the data volume and query frequency? What's the latency requirement? Then propose a schema design, explaining your choices. Use dimensional modeling patterns (fact/dimension tables) appropriate for analytics. Discuss normalization vs denormalization—normalization saves storage but denormalization improves query speed. For senior level, consider scalability: how would this design handle 10x data growth? Discuss partitioning strategy (by date, user_id, etc.) and indexing. Draw clear diagrams and explain join patterns. Be ready to justify decisions and consider trade-offs. Mention specific Meta products when discussing scale.

Focus Topics

Handling Data Quality & Consistency

Design schemas incorporating data quality from the ground up. Include metadata (load timestamps, data quality indicators, row counts). Discuss approaches: NOT NULL constraints, unique constraints, foreign keys. Design change tracking (which user made what change). For senior level, consider data lineage and impact of data quality issues on downstream analytics.

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Indexing for Query Optimization

Understand indexing strategies: single-column indexes, composite indexes, covering indexes, and when to create them. Know the cost of indexes (storage, write performance) vs benefits (read performance). Design indexes supporting key query patterns without excessive overhead. Discuss trade-offs between query speed and insert/update performance.

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Normalization vs Denormalization Trade-offs

Understand database normalization (1NF, 2NF, 3NF) and when to denormalize. Discuss trade-offs: normalized schemas reduce storage and ensure data consistency but require more joins; denormalized schemas are faster for reads but duplicate data. Make conscious trade-off decisions based on access patterns and update frequency. For analytics, discuss why denormalization often makes sense.

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Star Schema & Dimensional Modeling

Design star schemas with fact tables (measurable events/transactions) and dimension tables (descriptive attributes). Understand slowly changing dimensions (SCD Type 1, Type 2, Type 3). Know when to use dimension tables vs embedding attributes directly. For senior level, handle complex dimensional hierarchies and slowly changing dimensions in large-scale systems.

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Partitioning & Sharding Strategy

Design partitioning strategies for large tables: partition by date (most common for time-series data), user_id (for user-centric analyses), or other natural segments. Understand sharding for distributed systems and consistent hashing. Discuss benefits (query pruning, parallel processing) and trade-offs (uneven data distribution, query complexity across partitions).

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Onsite Round 2 - System Design & Data Pipeline Architecture

60 min6 focus topicssystem design

What to Expect

Second onsite round focusing on designing large-scale data pipeline systems. In this 50-60 minute interview, you'll design end-to-end data systems: how to ingest data from sources, transform it, store it, and expose it for consumption. Expect questions like 'Design a data platform to compute engagement metrics for Reels in near real-time' or 'Architect a system that logs and aggregates ad-performance data for downstream consumers'. You'll discuss data ingestion patterns (batch vs streaming), transformation logic, storage layers (data lakes, warehouses), orchestration (Airflow, DAGs), monitoring, and reliability. Expect to draw architecture diagrams and explain technology choices. Interviewers assess your ability to design scalable, reliable systems handling petabyte-scale data.

Tips & Advice

Start with clarifying questions: data volume, latency requirements (batch vs real-time), data sources, and end consumers. Propose a layered architecture: ingestion → transformation → storage → serving. For senior level, discuss scalability, reliability, and operational complexity. Address challenges: how do you handle data quality? Monitor pipeline health? Retry failures? Ensure exactly-once semantics? Discuss technology choices (Spark for batch, Kafka for streaming, Airflow for orchestration, data lakes vs warehouses). Draw clear architecture diagrams showing data flow. Explain trade-offs (complexity vs performance, real-time vs batch). Discuss observability: metrics, logging, alerting. Be ready to deep-dive into specific components.

Focus Topics

Data Lineage & Impact Analysis

Design systems tracking data lineage: which source tables feed which downstream tables? If a source table has bad data, which downstream systems are affected? Implement lineage tracking for debugging and impact analysis. For senior level, comprehensive lineage enabling quick root-cause analysis of data issues.

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Data Orchestration & Workflow Management (Airflow DAGs)

Design DAGs (Directed Acyclic Graphs) for orchestrating complex multi-step pipelines. Understand task dependencies, scheduling, monitoring, and retry logic. Use tools like Airflow. Design for fault tolerance: if step 3 fails, how do you recover? Understand incremental loads and checkpoint management. For senior level, manage hundreds or thousands of interdependent jobs.

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Big Data Technologies Stack (Spark, Hadoop, Cloud Platforms)

Understand distributed computing frameworks: Apache Spark (in-memory processing, faster than Hadoop), Hadoop (batch processing at scale). Understand cloud platforms: AWS (S3, RDS, Redshift, Glue), Azure, GCP. Know when to use each technology. Design for scalability across these platforms. For senior level, optimize cluster sizing, resource allocation, and cost.

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Batch vs Streaming Architecture Trade-offs

Understand batch processing (Spark, Hadoop) for high throughput, complete data transformation, and cost efficiency. Understand streaming (Kafka, Flink) for low latency and real-time metrics. Discuss trade-offs: batch is simpler, cheaper, but has higher latency; streaming is complex, more expensive, but real-time. Know when each is appropriate (real-time dashboards vs overnight aggregations).

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ETL Pipeline Architecture & Design Patterns

Design end-to-end ETL systems: Extract (ingestion from sources), Transform (cleaning, aggregation, enrichment), Load (into target systems). Understand common patterns: lambda architecture (batch + streaming), kappa architecture (streaming-only), medallion architecture (bronze/silver/gold layers). For senior level, design pipelines handling petabyte scale and supporting diverse downstream use cases.

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Data Quality, Monitoring & Reliability

Design monitoring for data pipelines: check for missing data, late arrivals, duplicate records, schema mismatches, and outliers. Build alerting systems notifying teams of issues. Design recovery mechanisms for pipeline failures. Implement data quality tests (row counts match, no unexpected NULLs, values in valid range). For senior level, comprehensive monitoring and SLA management.

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Onsite Round 3 - Product Analytics & Metrics Design

60 min5 focus topicstechnical

What to Expect

Third onsite round focusing on product analytics, metric definition, and product sense. In this 50-60 minute interview, you'll demonstrate understanding of how data drives product decisions at Meta. Expect questions like 'How would you measure success of a new Instagram Reels feature?' or 'Design a dashboard highlighting Facebook user behavior trends'. You'll define KPIs, discuss dashboard design, identify how to detect anomalies, and think through product tradeoffs. Interviewers assess your ability to think like both an engineer and an analyst—connecting technical data infrastructure to business impact. You'll discuss metrics, dimensional analysis, and how data insights drive product decisions.

Tips & Advice

Demonstrate Meta-specific product knowledge. Ask clarifying questions about the product goal. Think through multiple metrics addressing different aspects of success. Define metrics precisely (e.g., DAU = unique users with at least one action in 24 hours). Discuss why each metric matters. For dashboards, consider: what's the primary question? Who uses it? How frequently? Design for clarity and actionability. For anomaly detection, explain your approach. For senior level, connect to business strategy and discuss trade-offs (engagement vs retention, short-term vs long-term). Show you understand Meta's business (advertising revenue model, user engagement). Discuss how data informs product strategy.

Focus Topics

Experimentation & Causal Inference

Understand A/B testing design: control vs treatment groups, statistical significance, power analysis, minimum detectable effect size. Design experiments measuring feature impact. Understand challenges: network effects (user behavior affects other users), carryover effects, multiple comparisons. For senior level, complex experimental designs and statistical rigor.

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Dashboard Design & Data Visualization for Insights

Design dashboards answering specific business questions. Consider: primary metrics (what's the top line?), secondary metrics (what's driving it?), filters/dimensions (segment by country, device, etc.). Design for different audiences: executives need summaries, product managers need drill-down capability. Discuss update frequency (real-time vs daily). Understand visualization choices (line charts for trends, bar charts for comparisons, etc.).

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Product Understanding & Business Impact Connection

Demonstrate deep understanding of Meta's products and business model. Know how Meta monetizes (advertising), key products (Facebook, Instagram, WhatsApp, Reels), and business metrics (user growth, engagement, ARPU). Connect technical metrics to business outcomes. For senior level, understand strategy trade-offs (user acquisition vs monetization, engagement vs privacy, etc.).

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Anomaly Detection & Diagnosis

Design approaches to detect metric anomalies: statistical methods (z-score, moving averages, control limits), machine learning approaches, manual review. For anomalies, design diagnostic questions: is it real or a data quality issue? If real, what's causing it? Design drill-down analytics: segment by country, user cohort, traffic source. For senior level, automated anomaly detection and alerting systems.

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Metric Definition & KPI Design

Define clear, measurable metrics (KPIs) for product features. Understand different metric types: engagement (DAU, MAU, time spent), monetization (ARPU, total revenue), retention, growth. Define metrics precisely with numerator/denominator clarity. For senior level, design metric hierarchies supporting different stakeholders (executives need high-level metrics, product managers need detailed metrics). Understand lag time and leading indicators.

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Onsite Round 4 - Behavioral & Leadership

50 min6 focus topicsbehavioral

What to Expect

Fourth onsite round assessing behavioral fit, leadership capabilities, and cultural alignment. In this 40-50 minute interview, interviewers explore your past experiences through behavioral questions. Expect questions about conflict resolution, cross-functional collaboration, handling ambiguity, mentoring, and how you've influenced decisions. Questions like 'Tell me about a time you had to work with a difficult team member' or 'Describe a decision you made that influenced product strategy'. For senior level, expect focus on leadership: How have you grown as an engineer? How do you mentor others? How do you drive decisions in ambiguous situations? Interviewers assess cultural fit, growth mindset, and ability to work in Meta's collaborative environment.

Tips & Advice

Use the STAR method (Situation, Task, Action, Result) for behavioral questions. Prepare 4-5 stories showcasing different competencies: technical leadership, cross-functional collaboration, handling ambiguity, conflict resolution, mentoring. For each story, emphasize your role and impact. Discuss what you learned. Show growth mindset—talk about mistakes and how you improved. For senior level, emphasize: owning large initiatives end-to-end, mentoring junior engineers, influencing architectural decisions, cross-team collaboration. Show you lift others up. Discuss your philosophy on engineering and leadership. Ask thoughtful questions about team dynamics and growth opportunities. Be authentic; avoid over-rehearsed answers.

Focus Topics

Growth Mindset & Learning from Failures

Share a significant mistake or failure. What did you learn? How did you improve? Show humility and growth. For senior level, discuss how you help teams learn from failures psychologically safely. Emphasize continuous learning despite expertise.

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Conflict Resolution & Difficult Situations

Discuss conflicts you've navigated: technical disagreements, resource conflicts, cross-team issues. How did you approach them? Whose perspective did you consider? Did you change your mind? For senior level, discuss facilitating resolution between teams, not just personal conflicts.

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Handling Ambiguity & Making Decisions with Incomplete Information

Describe situations where requirements were ambiguous or changing. How did you gather information? Make decisions with incomplete data? Adapt? For senior level, discuss leading through ambiguity, helping teams move forward despite uncertainty.

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Cross-Functional Collaboration & Communication

Share experiences collaborating with data scientists, product managers, analytics engineers, infrastructure teams, etc. How did you align different perspectives? Resolve disagreements? Ensure technical decisions served business needs? For senior level, discuss influencing decisions across organizations, not just within your team.

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Mentoring & Developing Others

Describe experiences mentoring junior engineers or colleagues. How did you help them grow? What progress did you see? For senior level, discuss mentoring multiple people, helping them advance their careers. Show genuine interest in growing others. Discuss your mentoring philosophy.

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Ownership & Project Leadership at Senior Level

Demonstrate how you've owned substantial projects end-to-end, from conception to shipping. Discuss a project where you: identified the problem, designed the solution, led the implementation, overcame obstacles, and measured impact. Show accountability for outcomes. For senior level, discuss projects spanning quarters/years and impacting multiple teams.

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Frequently Asked Data Engineer Interview Questions

Advanced SQL Window FunctionsHardSystem Design

59 practiced

You have an ad-hoc analytical query that uses multiple window functions and takes minutes on a 1TB fact table. Design a solution to make these analytics interactive (sub-5 second) for analysts. Consider pre-aggregation, partitioning, materialized views, incremental refresh, and cost trade-offs.

Sample Answer

Requirements / constraints:- Interactive responses <5s for ad-hoc analytics over a 1TB fact table with multiple window functions.- Freshness: e.g., near-real-time vs hourly.- Cost sensitivity vs. developer ops effort.

High-level solution:1. Move heavy work into precomputed artifacts stored in a columnar, query-optimized store (e.g., Snowflake/BigQuery/Redshift Spectrum or Parquet on S3 with DuckDB/Trino).2. Create tiered materializations: - Low-latency materialized views (MVs) for common GROUP BY + window patterns (daily/hourly grain, needed dimensions). - Aggregate rollups (cube/OLAP) at multiple granularities (hour/day, region/product) to eliminate full-table scans. - Lightweight incremental sketches (top-k, approx distinct) for exploratory queries.

Architecture / components:- Ingestion -> raw landing -> CDC/stream processing (Debezium/Kinesis) -> staging.- Batch/stream jobs (Spark, Flink, dbt) maintain MVs and aggregate tables with incremental refresh using partition pruning (date partitions) and upserts (MERGE/CDC).- Query layer: Analysts hit a query engine with optimizer rules that rewrite queries to use MVs/rollups first; if not available, fall back to columnar base tables with proper partition pruning and vectorized execution.

Key techniques:- Partitioning: partition fact by date (and maybe region) and cluster/sort by join keys to speed window functions and range scans.- Materialized views: precompute window results where windows are stable (running totals, rank by partition); keep MVs incremental using partition-level refresh to avoid reprocessing full 1TB.- Incremental refresh: compute diffs using watermark/CDC; only recompute partitions touched since last run.- Query rewrite / catalog: maintain metadata to route queries to matching materializations; use cost-based selection to avoid stale MVs.- Caching: store hot query results in an in-memory cache (Redis) for sub-second repeated queries.

Trade-offs / cost:- Storage & compute: MVs and rollups increase storage and refresh compute costs but reduce per-query cost dramatically. Balance by materializing only high-value patterns and lower granularity.- Freshness vs cost: more frequent incremental refresh increases compute; choose hourly for most dashboards, real-time only for critical flows.- Complexity: maintenance of MV catalog and incremental pipelines adds engineering overhead; mitigate with dbt + CI and monitoring.

Example incremental refresh flow:- Detect changed partitions via CDC/watermark.- Run Spark job to compute aggregates/window functions for changed partitions and MERGE into MV (partition-level).- Update MV metadata and invalidate cache.

Expected outcome:- Most analyst queries hit precomputed aggregates or MVs and return <5s.- Uncommon ad-hocs still possible (longer), with guidance to create new materializations if repeatedly used.

When to consider alternatives:- If cardinality or query variability is extreme, consider a real OLAP engine (Druid/ClickHouse) or cube systems (Apache Pinot) optimized for fast aggregations and windowing.

Data Pipeline ArchitectureMediumSystem Design

55 practiced

Design a data retention and lifecycle policy for a data lake on S3 with zones: raw (immutable ingests), curated (parquet/Iceberg), and analytics (materialized tables). Define retention durations, partitioning schemes, compaction cadence, transition to cold storage, and automation for lifecycle transitions while supporting regulatory holds and auditability.

Sample Answer

Requirements & constraints:- Keep raw immutable for lineage; curated as query-optimized columnar (Parquet/Iceberg); analytics as materialized tables (e.g., Hive/Glue/Athena/Redshift/MSSQL).- Support regulatory holds (indefinite retention per record/stream), auditability, cost optimization, and query performance.

High-level policy (per zone):- Raw (immutable ingest) - Retention: default 2 years; configurable per dataset. - Storage: S3 Standard for first 30 days, then S3 Standard-IA; after 90 days move to S3 Glacier Flexible Retrieval; after 2 years move to Glacier Deep Archive or delete. - Partitioning: by ingestion_date (yyyy/MM/dd) and source_id to limit scan. - Compaction: none for raw (preserve original files), but optional nightly consolidation into daily bundles for reduced file count.- Curated (Parquet / Iceberg) - Retention: default 7 years for business datasets; 10+ years for regulated datasets. - Storage: S3 Standard for 90 days, Standard-IA up to 1 year, Glacier Flexible then Deep Archive per policy. - Partitioning: date-based (event_date), plus hashed partition for high-cardinality keys (e.g., region or tenant) or use Iceberg partition transforms. - Compaction cadence: daily micro-batch compaction (for streaming inputs) and weekly full compaction: merge small files into larger columnar files (e.g., target 256MB) using Spark jobs; run Iceberg/Delta compaction and major clean-up (delete tombstones) monthly. - Metadata: retain Iceberg snapshots for 30-90 days; expire older snapshots automatically.- Analytics (materialized tables) - Retention: shorter lifecycle (1–3 years) depending on usage; refresh cadence aligned to upstream. - Storage: S3 Standard during active period, move to Standard-IA after 30–90 days. - Partitioning and binning tuned for query patterns (date / customer_id). - Compaction: run on-demand before refresh or monthly to optimize query engines.

Automation & enforcement:- Use S3 object tags and prefixes to indicate zone, dataset, retention_class, regulation_flag, and last_accessed.- S3 Lifecycle rules: implement prefix/tag-based transitions (Standard → IA → Glacier → DeepArchive) and expiration for deletion. Keep short transitions in Standard for hot data.- Use AWS Glue / Apache Hive metastore + Iceberg to manage table metadata and execute snapshot expiration/optimization commands.- Orchestrate compaction and lifecycle via Step Functions or Airflow: - Triggers: time-based schedules + event-driven (e.g., arrival of X MB). - Jobs: Spark/EMR/Databricks compaction, Iceberg rewrite-manifest, snapshot expiration. - Post-job: update tags/metrics and emit events to audit log.- Regulatory holds & legal compliance: - Implement S3 Object Lock (governance/compliance mode) at object level for datasets under legal hold; record holds in a centralized holds catalog (DynamoDB) with TTL disabled until release. - For table-level holds, mark dataset retention_class=“hold” to prevent lifecycle transitions; lifecycle automation checks hold catalog before actions. - All manual/automated hold actions require RBAC and MFA enforced approvals (via IAM + workflow).- Auditability & monitoring: - Enable CloudTrail, S3 Access Logs, and Data Lake metadata logging. Store logs in a separate, immutable audit bucket with longer retention. - Emit metrics (file counts, small-file ratio, last compaction, storage class sizes) to CloudWatch/Prometheus and alert on anomalies or failed jobs. - Periodic data quality and retention reports delivered to compliance stakeholders.- Edge cases & operational notes: - Support restores from Glacier with expected latency SLAs; mark datasets needing faster restore separately. - For high-cardinality/time-series, prefer Iceberg with hidden-partition and partition evolution to avoid expensive rewrites. - Test lifecycle rules in staging; include dry-run and simulation prior to production changes.

This design balances cost (tiered storage + compaction) with performance (curated materialization and partitioning), while providing deterministic automation, audit trails, and a robust process for regulatory holds.

Data Pipeline and Data QualityMediumTechnical

28 practiced

Describe strategies and code-level techniques to gracefully handle schema drift in incoming JSON payloads during ingestion: new fields added, nested structures changed, type changes, and unexpected fields. Include versioned parsers, fallback schemas, schema registry usage, and how to alert owners when changes occur.

Sample Answer

Approach: treat ingestion as schema-tolerant and observable. Combine defensive parsing, versioned parsers, a schema registry, fallback schemas, and automated alerts so owners act when drift occurs.

Code-level techniques (example in Python):- Use a schema registry (e.g., Confluent Schema Registry / custom service) to fetch expected JSON schema per topic/version.- Implement versioned parsers that map known schema -> internal model; keep old parsers for backward compatibility.- Defensive parsing: validate, coerce types, capture unknown fields into an "extras" map, and optionally drop or store them.

Example:

python

import jsonschema, json

SCHEMAS = {"v1": {...}, "v2": {...}}  # load from registry

def parse_payload(raw, version_hint=None):
    obj = json.loads(raw)
    version = version_hint or obj.get("_schema_version") or latest_version_for_topic()
    schema = SCHEMAS.get(version)
    if not schema:
        alert_owner("missing_schema", version, raw)
        schema = fallback_schema()  # best-effort
    try:
        jsonschema.validate(obj, schema)
    except jsonschema.ValidationError as e:
        # record validation errors, try coercions
        obj = coerce_types(obj, schema)
        log_validation_error(e, obj)
        if still_invalid(obj, schema):
            route_to_dead_letter_queue(obj, reason=str(e))
            return None
    # keep unexpected fields
    extras = {k:v for k,v in obj.items() if k not in schema["properties"]}
    return normalize(obj, extras, version)

Key strategies:- Versioned parsers: encapsulate parsing logic per schema version so migration is explicit and reversible.- Fallback schema: a permissive schema (allow additionalProperties) used only for short-term ingestion + enrichment pipeline.- Store unknown fields in a raw blob column or data lake to avoid data loss.- Schema registry: centralize authoritative schemas and enforce via CI (tests) and runtime fetching; support compatibility rules (backward/forward).- Automatic alerts: on schema mismatch/validation failures, send structured alerts (PagerDuty/Slack/email) with sample payload and diffs; create dashboards (counts of new fields, type-change rates) and thresholds for escalation.- Telemetry: emit metrics (validation_failures, unknown_field_rate, new_field_names) and use anomaly detection to surface sudden drift.- Operational playbook: owners receive alert + link to sample payload + suggested schema change PR template.

Trade-offs:- Strict validation prevents bad data but risks loss; permissive fallback avoids data loss but increases downstream complexity. Use hybrid: strict for critical fields, permissive for optional fields.

This approach keeps pipelines resilient, prevents silent data loss, and ensures schema changes are tracked and owned.

Cross Functional Collaboration and CoordinationHardTechnical

49 practiced

Propose a framework to measure the effectiveness and health of cross-functional collaboration on data engineering initiatives. Include measurable indicators (both leading and lagging), data sources, reporting cadence, visualization ideas, thresholds for intervention, and an example of a corrective action triggered by a metric breach.

Sample Answer

Framework overview: measure collaboration health across three lenses — delivery (velocity & quality), communication & alignment, and trust & ownership. Combine leading indicators (predictive) and lagging indicators (outcomes) to drive timely intervention.

Measurable indicators- Leading - Sprint/task throughput variance: % of data tasks planned vs started within sprint (source: Jira) - Blocker age and count: # unresolved cross-team blockers >48h (ticketing) - Cross-team review turnaround: avg PR review time for shared modules (Git) - Onboarding time for new consumers: days-to-first-successful-query (analytics logs)- Lagging - Pipeline reliability: MTTR and MTBF for production pipelines (observability logs) - Data quality incidents: # incidents per month, SLO compliance (DQ tooling) - Time-to-deliver data product: request→production lead time (tickets + CI/CD) - Consumer satisfaction: NPS or CSAT from quarterly surveys

Data sources- Jira/Asana, Git/GitHub/GitLab, CI/CD and deployment logs, Datadog/Prometheus/CloudWatch, data quality tools (Great Expectations), service catalog, stakeholder survey platform.

Reporting cadence & owners- Weekly: leading indicators dashboard (data eng owner) for operational tuning - Biweekly: cross-functional sync review (squad leads) — blockers & roadmap alignment - Monthly: health report with lagging indicators + trend analysis (engineering manager) - Quarterly: stakeholder satisfaction and strategic review (EM + PM + analytics lead)

Visualization ideas- KPI heatmap by team (green/yellow/red), trendlines with rolling 4-week windows, Sankey for handoffs and wait times, stacked bar for incident categories, calendar of blocker aging, funnel for request→production.

Thresholds for intervention (examples)- Blockers >48h: alert; >5 concurrent for org = immediate triage meeting - PR review avg >48h or >30% stalled PRs: trigger dedicate review week / reduce WIP - Pipeline MTTR > SLO (e.g., 4h) for two consecutive weeks: escalate to reliability task force - Consumer CSAT <70%: initiate listening sessions + root-cause analysis

Corrective action exampleMetric breach: monthly data quality incidents >5 (threshold). Action:1) Immediate: create incident war room, assign an owner, patch the critical pipeline to stop bad downstream consumption (rollback or quarantine dataset). 2) Short-term (1–2 weeks): run RCA, tag incidents by root cause (ingestion, schema change, code bug), add automated tests and schema checks to CI for failing class. 3) Medium-term (1 quarter): adjust SLA, add monitoring and alerting to detect anomaly earlier, and schedule cross-functional postmortems with data consumers to adjust contract/guidelines. Measure success by reduction to <2 incidents/month and improved CSAT by next quarter.

Why this works- Mix of leading/lagging gives early warning and outcome validation. - Clear owners, cadence and thresholds reduce ambiguity and speed remediation. - Visualizations surface bottlenecks and drive data-informed conversations, not blame.

Data Pipeline Monitoring and ObservabilityEasyTechnical

25 practiced

Define the core pipeline metrics you would collect for most ETL/ELT and streaming jobs: throughput, end-to-end latency, per-stage processing time, error rate, backlog/lag, and data volume. For each metric state typical units, where you would instrument it (producer, broker, consumer, job), and give a practical example alert threshold for both a nightly batch job and a low-latency streaming job.

Sample Answer

Throughput- Units: records/sec or MB/sec- Instrumentation: producer (emit rate), broker (ingest rate), consumer/job (processed rate)- Alerts: Nightly batch — alert if average < 80% of expected/job spec for >30m. Streaming — alert if sustained drop >50% for >1m.

End-to-end latency- Units: seconds or milliseconds (depending on workload)- Instrumentation: timestamp at producer + timestamp at final sink; measure in job/consumer and end-to-end tracing system- Alerts: Nightly batch — alert if job runtime > configured SLA (e.g., > 2x expected runtime). Streaming — alert if 95th percentile latency > 500 ms for >5m.

Per-stage processing time- Units: ms or sec per stage/batch- Instrumentation: instrument start/end in each pipeline stage (ETL task, Spark stage, Kafka consumer)- Alerts: Nightly — any stage > 3x historical mean for that stage. Streaming — single stage 99th percentile > 200 ms or contributes >30% of total latency.

Error rate- Units: errors per million events (ppm) or % of events- Instrumentation: producers (validation errors), jobs (processing exceptions), sinks (write failures)- Alerts: Nightly — >0.5% failed records or >fixed absolute count (e.g., >1000) triggers paging. Streaming — >0.1% for >5m triggers immediate alert.

Backlog / lag- Units: number of records/messages or offset lag (partitions behind) or time lag (seconds)- Instrumentation: broker (queue length, partition lag), consumer/job (committed offset vs head)- Alerts: Nightly — backlog not drained within maintenance window. Streaming — partition lag > 1M messages or time lag > 5 minutes for >2m.

Data volume- Units: MB/GB per hour or records per run- Instrumentation: producer metrics, broker ingress, job input/output counters, storage metrics- Alerts: Nightly — input volume deviates ±30% from expected for that source. Streaming — sudden drop >50% or spike >200% sustained >10m (indicates upstream issue).

Notes: choose concrete SLA numbers based on historical baselines and business impact; use percentile-based alerts (p95/p99) for latency; combine alerts (e.g., backlog + throughput) to reduce noise.

Batch and Stream ProcessingEasyTechnical

75 practiced

Explain the difference between stateless and stateful operators in streaming pipelines. Provide examples of each (e.g., filter vs aggregation/session window) and discuss operational consequences for scaling, failure recovery, checkpointing, and state size limits.

Query Optimization and Execution PlansMediumTechnical

92 practiced

You are reviewing a query plan that shows a sequence of index scans on many small indexes (bitmap/parallel operations). Explain how bitmap index scans work and why they can be faster than multiple independent index scans plus merges for highly selective multi-column predicates.

Advanced SQL Window FunctionsMediumTechnical

58 practiced

Provide a safe, production-ready SQL pattern to delete duplicate records in large tables using window functions in batches. Table: events(id bigserial primary key, event_key text, created_at timestamp). Explain batching, transaction sizing, and how to resume on failure.

Sample Answer

Approach: use a window function (ROW_NUMBER() partitioned by event_key ordered by created_at ASC) to identify duplicates (rows with rn > 1), delete them in controlled batches (LIMIT per transaction) so you avoid long locks and large WAL spikes. Use ctid to safely target physical rows.

Example safe, resumable SQL loop (Postgres):

sql

-- Run inside a transaction per batch; repeat until 0 rows deleted.
WITH duplicates AS (
  SELECT ctid
  FROM (
    SELECT ctid,
           ROW_NUMBER() OVER (PARTITION BY event_key ORDER BY created_at, id) AS rn
    FROM events
  ) t
  WHERE rn > 1
  ORDER BY ctid
  LIMIT 10000  -- batch size
)
DELETE FROM events e
USING duplicates d
WHERE e.ctid = d.ctid;

How to run in production (bash/psql loop or plpgsql job):- Use an external controller (cron, Airflow, or a safe plpgsql DO loop) that runs the above DELETE repeatedly until affected_rows = 0.- Monitor affected row count each iteration; stop when zero.

Batching and transaction sizing:- Choose batch_size based on WAL, replication lag, and lock time. Start small (1k–10k) and observe. Aim for transactions under 30s.- Each DELETE is a full transaction here; keeps rollback segments small and reduces impact.- If using replicas, watch replication lag; reduce batch_size if lag increases.

Resume on failure:- Because each batch is committed, you can safely resume by re-running the loop; it will only delete remaining duplicates.- For deterministic progress tracking in case you need precise resume: populate a tombstone table of deleted ids/ctids per batch or record last_run timestamp and removed_count.- Alternatively, add a temporary boolean column (is_deduped) or use a checksum to mark processed keys before deleting — helpful if identifying duplicates is expensive.

Safety tips:- Ensure an index on (event_key, created_at, id) to speed window computation.- Test on a snapshot, run EXPLAIN ANALYZE, and run during low-traffic windows.- Keep regular backups or make the operation reversible via backup before large runs.

Data Pipeline ArchitectureHardTechnical

48 practiced

Compare Avro, Parquet, and ORC across common pipeline workloads: streaming ingestion, OLAP analytical queries, and ML feature store storage. For each format discuss columnar vs row characteristics, compression, predicate pushdown, schema-evolution support, and read/write performance trade-offs.

Sample Answer

Situation: When choosing a storage format for pipelines, I evaluate trade-offs across workloads: streaming ingestion, OLAP queries, and ML feature-store storage. Below is a focused comparison of Avro, Parquet, and ORC across columnar vs row characteristics, compression, predicate pushdown, schema evolution, and read/write trade-offs.

Summary by format:- Avro: - Row-oriented (record-based), optimized for write-heavy, streaming ingestion and RPC. - Compression: block-level codecs (snappy, deflate) yield small write/serialize overhead; good for transport. - Predicate pushdown: limited—row format prevents efficient column pruning; can still filter at record level. - Schema evolution: excellent—explicit schema with forward/backward compatibility, default values, field-level changes. - Performance trade-offs: very fast writes and low-latency streaming; not ideal for analytical scans or columnar-heavy ML feature reads.

- Parquet: - Columnar, page/column chunk layout, excellent for OLAP and analytical scans. - Compression: column-level codecs (snappy, gzip, zstd, brotli) with per-column encoding (dictionary, RLE) — high compression and CPU tradeoffs. - Predicate pushdown: strong — statistics (min/max, null counts) enable skip of row groups and pages. - Schema evolution: good — supports adding nullable columns; renames/complex changes require care (tooling like Spark helps). - Performance trade-offs: read-optimized, great for selective column reads and aggregation; writes are heavier (buffering/columnization), not ideal for per-record streaming.

- ORC: - Columnar like Parquet, with sophisticated stripe/column index structures. - Compression: excellent — built-in codecs (snappy, zlib, zstd) plus lightweight encodings; often slightly better compression than Parquet for some workloads. - Predicate pushdown: very strong — fine-grained column statistics and lightweight indexes enable aggressive skipping. - Schema evolution: solid — supports adding/removing fields, but cross-engine compatibility varies. - Performance trade-offs: optimized for fast analytical reads and vectorized processing (Hive/Spark); write cost similar to Parquet, sometimes faster reads for complex nested types.

Workload recommendations:- Streaming ingestion: Avro (low-latency writes, schema registry). If you must produce columnar files for downstream OLAP, buffer and convert to Parquet/ORC in micro-batches.- OLAP analytical queries: Parquet or ORC. Choose ORC where Hive/Hadoop ecosystem and heavy aggregation/vectorized SQL dominate; choose Parquet for broad cross-platform support (Spark, Presto, BigQuery).- ML feature store storage: Parquet often preferred (columnar reads, wide tool support, fast selective retrieval). ORC is competitive if your stack favors it. For online feature store (low-latency row reads), use a KV store or Avro/row-store for fast single-row access.

Final trade-offs:- Avro = write/streaming, schema evolution, low-latency per-record IO.- Parquet = cross-platform columnar balance, wide tooling, good compression & predicate pushdown.- ORC = aggressive columnar optimizations, slightly better compression/indexing for heavy OLAP.

In practice, use Avro for ingestion + schema registry, then convert to Parquet/ORC for analytical/feature-store workloads; pick Parquet for portability, ORC for highest OLAP throughput in Hadoop-native environments.

Data Pipeline and Data QualityMediumTechnical

43 practiced

Explain components of an enterprise data governance program relevant to data engineering: ownership and stewardship, metadata and cataloging, access controls and RBAC, PII discovery and handling, SLAs/SLOs, data contracts, and policy enforcement across pipelines. Provide practical steps to start governance at a mid-sized company.

Sample Answer

A solid enterprise data governance program for data engineering covers people, metadata, security, quality, and enforcement. Key components and what they mean for a data engineer:

- Ownership & stewardship: Assign data owners (product/line managers) for accountability and data stewards (technical) who implement cleaning, schemas, and lineage. Engineers rely on owners for business rules and stewards for pipeline-level validation.- Metadata & cataloging: Central catalog (e.g., Data Catalog, Amundsen, DataHub) that stores schema, lineage, tags, data quality metrics, and sample data. Enables discoverability and automates lineage for impact analysis.- Access controls & RBAC: Enforce least privilege via IAM and fine-grained controls (column/table-level) using tools like AWS Lake Formation, GCP IAM, Privacera. Engineers implement role-aware pipelines and automated access provisioning.- PII discovery & handling: Automated scanners (regex, ML-based) to tag PII, mask/tokenize/encrypt per policy. Integrate discovery into CI/CD so sensitive fields are treated differently in dev/test vs prod.- SLAs / SLOs: Define freshness, latency, completeness SLAs for datasets and monitor with observability (Prometheus, Grafana, Monte Carlo). Alerting drives remediation runbooks.- Data contracts: Schema/version contracts between producers and consumers (schemas in Schema Registry, protobuf/Avro, contract tests) to prevent breaking changes.- Policy enforcement across pipelines: Policy-as-code (Open Policy Agent), CI checks, and runtime guards (policy checks in orchestration like Airflow) to block non-compliant deployments.

Practical steps to start at a mid-sized company:1. Kickoff: Form a cross-functional steering group (engineering, analytics, legal, product).2. Inventory: Run a quick scan to catalog critical datasets and owners.3. Prioritize: Pick 3 high-value datasets and apply full governance (cataloging, SLA, PII scan, access rules).4. Implement low-friction tooling: Deploy a metadata catalog + schema registry and integrate with existing pipelines.5. Automate checks: Add contract tests, PII detection, and policy checks into CI/CD.6. Monitor & iterate: Define SLOs, add monitoring/alerts, and run monthly reviews; expand scope gradually.

This approach balances business accountability with engineering automation so governance scales without blocking delivery.

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Meta Senior Data Engineer Interview Preparation Guide

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Recruiter Screening

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Communication Style & Cultural Fit

Practice Interview

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Motivation for Data Engineering

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Technical Phone Screen - SQL

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Query Optimization & Performance

Practice Interview

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Common Table Expressions (CTEs) & Recursive Queries

Practice Interview

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Practice Interview

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NULL Handling & Edge Cases

Practice Interview

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Complex Joins & Multi-Table Aggregations

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Technical Phone Screen - Python & Coding

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String Manipulation & Pattern Matching

Practice Interview

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Practice Interview

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Algorithm Design & Complexity Analysis

Practice Interview

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Python Data Structures & Collections

Practice Interview

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Onsite Round 1 - Data Modeling & Schema Design

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Practice Interview

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Indexing for Query Optimization

Practice Interview

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Normalization vs Denormalization Trade-offs

Practice Interview

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Star Schema & Dimensional Modeling

Practice Interview

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Partitioning & Sharding Strategy

Practice Interview

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Onsite Round 2 - System Design & Data Pipeline Architecture