FAANG Data Engineer Interview Preparation Guide - Mid Level
This guide is based on general FAANG interview practices and may not reflect specific company procedures.
FAANG companies typically conduct 8-9 interview rounds for mid-level data engineers, spanning technical assessments (coding, SQL, architecture design), case studies, and behavioral evaluations. Each round is designed to assess different competencies: coding proficiency, SQL and data modeling expertise, data pipeline architecture design, big data framework knowledge, cloud platform expertise, and cultural fit. The process emphasizes problem-solving approach, communication skills, ability to work across teams, and mentorship potential.
Interview Rounds
Recruiter Screening
What to Expect
This initial 30-minute phone call with a recruiter assesses your background, motivation, and basic understanding of the data engineer role. The recruiter will review your resume, discuss your previous experience with data pipelines, infrastructure, and technologies, and verify cultural alignment. Expect questions about why you're interested in data engineering, what excites you about building data systems at scale, and your career trajectory. This round serves as a filter to ensure you meet baseline requirements and have genuine interest in the role before proceeding to technical interviews.
Tips & Advice
Have a clear, concise elevator pitch highlighting your data engineering experience and genuine enthusiasm for building data infrastructure. Review the job description thoroughly and identify 2-3 specific aspects that excite you (e.g., working with petabyte-scale data, designing ETL pipelines, working with Spark, specific cloud platforms mentioned). Discuss your resume in detail, especially projects involving data pipeline development, ETL/ELT processes, data warehousing, or cloud data platforms. Prepare examples of how you've solved data infrastructure problems. Research the company's tech stack if possible and mention relevant experience. Be conversational and authentic—recruiters assess cultural fit and communication as much as technical qualifications. Prepare questions about the role and team that show genuine interest.
Focus Topics
Communication and Professional Presence
Articulating technical concepts clearly and professionally. Demonstrating confidence and enthusiasm without arrogance. Active listening to recruiter questions and authentic engagement in conversation.
Practice Interview
Study Questions
Motivation and Career Alignment
Clear understanding of why you want the data engineer role and how it aligns with career trajectory. Demonstrating thoughtfulness about growth opportunities, technical challenges, and company mission.
Practice Interview
Study Questions
Resume Deep Dive and Experience Walkthrough
Thorough understanding of your background including data pipeline projects, technologies used (Spark, Hadoop, cloud platforms), infrastructure built, metrics/impact achieved, and lessons learned. Connecting past experiences directly to role requirements.
Practice Interview
Study Questions
Technical Screening Call
What to Expect
A 60-minute remote technical interview where you'll solve 1-2 practical coding problems, typically one in Python and one in SQL. The Python problem often involves data manipulation and algorithmic thinking (similar to what you'd implement in a data pipeline). The SQL problem tests ability to write and optimize queries. You'll code in a shared environment, explain your thinking, discuss approaches, and optimize your solution. The interviewer assesses problem-solving methodology, coding fluency, understanding of data structures and algorithms, and ability to communicate technical ideas clearly. They're looking for clean code, correct logic, edge case handling, and optimization thinking.
Tips & Advice
Practice live coding on platforms like LeetCode, HackerRank, or CodeSignal, focusing on medium-difficulty problems involving data manipulation. Start by understanding requirements fully—ask clarifying questions about data size, constraints, and expected output format. Outline your approach verbally before coding, discussing time and space complexity estimates. Write clean, readable code with meaningful variable names. Test edge cases thoroughly (empty inputs, single elements, duplicates, negative numbers). If you get stuck, think out loud; interviewers value your problem-solving process over perfect code. For SQL problems, practice writing queries with CTEs, window functions, and proper indexing considerations. Focus on problems involving data filtering, aggregation, joining datasets, and handling missing data—all common in data engineering. Optimize progressively: first get the correct solution, then improve efficiency. Be ready to discuss further optimizations even after solving.
Focus Topics
Problem-Solving Approach and Code Communication
Systematically breaking down problems, clarifying requirements, discussing approach before coding, and explaining trade-offs. Demonstrating ability to incrementally debug and optimize. Asking clarifying questions and thinking out loud to show reasoning.
Practice Interview
Study Questions
Python Data Manipulation and Algorithms
Proficiency with Python data structures (lists, dictionaries, sets, tuples) and operations. Understanding algorithmic approaches to common problems (searching, sorting, counting, deduplication). Writing Pythonic, efficient code. Familiarity with libraries like collections (Counter, defaultdict) for efficient solutions.
Practice Interview
Study Questions
SQL Query Writing and Optimization
Writing correct SQL queries using WHERE, GROUP BY, HAVING, ORDER BY, JOINs. Understanding JOIN types and when to use each. Query optimization basics: indexes, execution plans, and efficient filtering. Writing CTEs and subqueries appropriately.
Practice Interview
Study Questions
SQL and Data Modeling Deep Dive
What to Expect
A 60-minute on-site or virtual interview focused on advanced SQL and database schema design. You'll tackle complex SQL problems involving multiple joins, window functions, CTEs, aggregations, and subqueries. You may also design database schemas for business scenarios, considering normalization, denormalization trade-offs, and query performance implications. The interviewer assesses SQL expertise, understanding of relational database concepts, ability to optimize queries, and schema design maturity. Expect problems like 'Design a database schema for an e-commerce platform' or 'Write a query to find the top-spending customer per month.' You should clearly communicate reasoning for design choices and discuss alternative approaches and their trade-offs.
Tips & Advice
Practice complex SQL extensively using LeetCode, Mode SQL Tutorial, or HackerRank. Master window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, RUNNING_SUM) and CTEs (WITH clauses). Understand different JOIN types and performance implications. Practice query optimization: use EXPLAIN plans to understand query execution, think about indexing strategies, and consider column selectivity. For schema design problems, start by understanding business requirements and identifying entities, relationships, and access patterns. Normalize appropriately but be ready to discuss denormalization trade-offs for analytical workloads. Use entity-relationship diagrams to communicate your design. Always discuss multiple approaches: why you chose this design over alternatives, what trade-offs you're making (query performance vs. storage, flexibility vs. complexity), and when you'd revisit the design. Practice writing queries and explaining execution plans out loud.
Focus Topics
Query Performance Optimization
Understanding query execution plans and how to read EXPLAIN output. Index design strategies (B-tree, hash) and when to use indexes. Identifying bottlenecks: full table scans, expensive joins, data skew. Proposing optimizations: better indexes, query restructuring, denormalization.
Practice Interview
Study Questions
Relational Database Schema Design and Normalization
Understanding normalization principles (1NF, 2NF, 3NF, BCNF), primary and foreign keys, relationships (one-to-one, one-to-many, many-to-many). Designing efficient schemas considering query patterns. Ability to identify and resolve schema design issues.
Practice Interview
Study Questions
Advanced SQL Query Writing
Writing complex SQL using window functions, CTEs, subqueries, set operations, and common table expressions. Handling multi-table joins, complex aggregations, time-series analysis, and ranking problems. Deep understanding of GROUP BY, HAVING, DISTINCT, and ORDER BY semantics.
Practice Interview
Study Questions
Data Pipeline Architecture and Design
What to Expect
A 75-minute technical interview where you design end-to-end data pipeline and architecture solutions for business problems. You might be asked: 'Design a data pipeline to ingest and process real-time clickstream data from a mobile app' or 'How would you build a data warehouse for an e-commerce company?' You'll clarify requirements, propose architecture (data sources → ingestion → storage → processing → consumption), justify technology choices, address scalability and reliability, and discuss data quality and monitoring strategies. The interviewer assesses architectural thinking, understanding of data engineering patterns (ETL vs. ELT, batch vs. real-time, data warehouse vs. data lake), technology trade-offs, and ability to design systems that handle scale. This is where you demonstrate systems thinking and engineering maturity.
Tips & Advice
Start by asking clarifying questions: What data volume? What's the required latency (real-time vs. batch)? What are key metrics and use cases? Who are consumers (analysts, scientists, business teams)? What's the current state? Draw a clear architecture diagram showing data flow from sources through processing to consumption. For each component, discuss technology choices with explicit reasoning. For example: 'We need Kafka for real-time data streaming because [reason]; S3 for data lake because [reason]; Spark for processing because [reason].' Address non-functional requirements: How does it scale if traffic increases 10x? How do you handle failures and retry logic? How do you monitor and alert? How do you ensure data quality? Discuss partitioning strategies for storage (by date, customer ID, etc.) based on access patterns. Consider batch vs. real-time trade-offs: latency requirements, complexity, costs. For batch: discuss scheduling, dependency management, incremental vs. full refreshes. For streaming: discuss event ordering, exactly-once vs. at-least-once semantics, backpressure handling. Mention lessons from real projects. Be prepared to pivot if questioned—discuss alternatives and why you didn't choose them. Reference trade-offs explicitly throughout.
Focus Topics
Data Quality, Validation, and Observability
Designing data quality frameworks with validation checks, anomaly detection, and SLAs. Building monitoring and alerting for pipeline failures, data quality issues, and performance degradation. Implementing observability: logging, metrics, traces. Designing debugging strategies.
Practice Interview
Study Questions
ETL vs. ELT Patterns and Processing Strategy
Understanding Extract-Transform-Load vs. Extract-Load-Transform approaches. Knowing when to transform before loading (ETL) vs. after (ELT). Trade-offs: data quality, flexibility, performance, and operational complexity. Choosing batch processing, streaming, or hybrid approaches based on requirements.
Practice Interview
Study Questions
Technology Selection and System Trade-offs
Comparing and selecting appropriate technologies (Kafka vs. Pub/Sub, Spark vs. Flink, Redshift vs. BigQuery vs. Synapse) based on requirements. Understanding trade-offs: cost, latency, complexity, operational overhead, team expertise, learning curve.
Practice Interview
Study Questions
Data Pipeline Architecture and Design Patterns
Designing complete data pipelines from ingestion through processing to consumption. Understanding architectural patterns: Lambda architecture (batch + real-time), Kappa architecture (streaming only), event streaming. Designing data flow, component interactions, and integration points. Considering scalability, reliability, and maintainability.
Practice Interview
Study Questions
Apache Spark and Distributed Processing
What to Expect
A 60-minute technical interview assessing proficiency with Apache Spark and distributed data processing. You'll solve coding problems using Spark DataFrames or RDDs, optimize Spark jobs, and discuss distributed computing concepts. Typical problems: 'Write a Spark job to process a large dataset and compute aggregations' or 'Optimize this Spark pipeline that's taking too long.' You may also discuss MapReduce concepts, Hadoop, and other distributed processing frameworks. The interviewer evaluates your understanding of distributed computing principles, Spark architecture, lazy evaluation, partitioning, shuffles, and ability to write scalable data processing code.
Tips & Advice
Practice PySpark or Scala extensively using DataCamp, Spark documentation, or by building sample projects. Understand RDDs vs. DataFrames vs. Datasets and when to use each (generally prefer DataFrames). Master DataFrame API: transformations (select, filter, flatMap, groupBy, agg) and actions (collect, count, write). Understand lazy evaluation: transformations don't execute until an action is called. Learn DAG (Directed Acyclic Graph) concept and how Spark builds execution plans. Practice optimizing Spark jobs: minimize shuffles (they're expensive), use narrow transformations when possible, partition appropriately, cache intermediate results when reused, broadcast small DataFrames for large lookups. Understand memory management and OOM errors. For interview problems, think about data distribution: how many partitions? Will it fit in memory? Explain your reasoning for partitioning and shuffle decisions. Discuss caching strategies and when to cache vs. when it's wasteful. Mention performance implications of operations (which cause shuffles, which are narrow, etc.).
Focus Topics
Spark Performance Tuning and Optimization
Techniques for optimizing Spark jobs: partitioning strategy, caching vs. persistence trade-offs, broadcast variables for efficient lookups, avoiding wide transformations, memory management. Understanding Spark UI for debugging and profiling. Handling data skew.
Practice Interview
Study Questions
Distributed Processing Patterns and Shuffle Optimization
Understanding map-reduce patterns and how Spark distributes computation. Understanding shuffle operations: how they work, why they're expensive, when they occur. Strategies to minimize shuffles: narrow transformations, repartitioning strategically, using broadcast joins for small DataFrames.
Practice Interview
Study Questions
Apache Spark Architecture and DataFrame/RDD Concepts
Understanding Spark's architecture: driver, executors, executor memory, partitions, and DAG. RDDs vs. DataFrames vs. Datasets: when to use each. Lazy evaluation and action vs. transformation distinction. Using DataFrame API for most operations due to Catalyst optimizer benefits.
Practice Interview
Study Questions
Cloud Data Platforms and Infrastructure
What to Expect
A 60-minute interview focused on cloud data platforms and infrastructure. You'll discuss services like AWS (Redshift, S3, EMR, Glue, Kinesis), Azure (Synapse, Data Lake Storage, Data Factory), or GCP (BigQuery, Dataflow, Cloud Storage, Pub/Sub). Expect questions: 'How would you build a data warehouse on AWS?' or 'Design a scalable data ingestion system on GCP.' You'll discuss technology choices, architecture design, cloud-native patterns, cost optimization, security, and governance. The interviewer assesses knowledge of cloud services, architectural decision-making, ability to leverage cloud capabilities effectively, and understanding of operational considerations like cost, compliance, and security in cloud environments.
Tips & Advice
Familiarize yourself thoroughly with at least one major cloud platform by working through tutorials, building sample projects, and understanding pricing. For AWS: study S3 (partitioning, prefixes), Redshift (architecture, distribution keys, sort keys), EMR (launching clusters, Spark on EMR), Glue (ETL jobs, crawlers, data catalog), Kinesis (streams, shards), Lambda. For Azure: learn Synapse (dedicated and serverless SQL pools), Data Lake Storage Gen2 (hierarchical namespace), Data Factory (pipelines, activities), Stream Analytics. For GCP: understand BigQuery (architecture, cost model, ML integration), Dataflow (Apache Beam), Cloud Storage (buckets, lifecycle policies), Pub/Sub. Build hands-on experience: create a data pipeline on your chosen platform, ingest real or sample data, run queries, understand cost implications. Understand cloud-native services vs. traditional approaches. Learn about serverless vs. managed options (trade-offs). Understand data formats for cloud storage (Parquet, ORC, CSV) and partitioning strategies. Learn security basics: IAM, encryption, VPCs. Discuss cost optimization: reserved capacity, data lifecycle management. Be ready to compare platforms for specific use cases—this shows thoughtful decision-making.
Focus Topics
Cloud Security, Governance, and Cost Management
Understanding cloud security practices (IAM roles and policies, encryption at rest and in transit, VPCs, network security). Data governance: access control, audit logging, compliance (GDPR, CCPA). Cost optimization strategies: reserved capacity, lifecycle policies, data tiering. Building secure, compliant data pipelines.
Practice Interview
Study Questions
Azure Data Platforms (Synapse, Data Lake Storage, Data Factory)
Understanding Azure's data ecosystem: Synapse for data warehousing with both dedicated and serverless SQL pools, Data Lake Storage Gen2 for enterprise data lakes, Data Factory for orchestration. Understanding how components integrate and when to use each for different workloads.
Practice Interview
Study Questions
GCP Data Services (BigQuery, Dataflow, Cloud Storage, Pub/Sub)
Proficiency with GCP data services: BigQuery for serverless data warehousing with built-in ML, Dataflow for Apache Beam pipelines, Cloud Storage for data lake, Pub/Sub for messaging. Understanding BigQuery's cost model and performance characteristics. Knowing when to use each service.
Practice Interview
Study Questions
Cloud Data Warehouse Design and Optimization
Designing scalable data warehouses on cloud platforms. Understanding denormalization for analytics (star schema, snowflake schema). Cloud-specific concepts: distribution keys (Redshift), sort keys, partitioning strategies, clustering (BigQuery). Handling incremental loading, slowly changing dimensions, and time-series data efficiently.
Practice Interview
Study Questions
AWS Data Services (Redshift, S3, EMR, Glue, Kinesis)
Proficiency with AWS data ecosystem: S3 for data lake storage and object lifecycle, Redshift for data warehousing and complex queries, EMR for managed Spark/Hadoop clusters, Glue for serverless ETL jobs and data catalog, Kinesis for real-time streaming. Knowing when to use each service and how to integrate them into cohesive solutions.
Practice Interview
Study Questions
Case Study and Project-Based Assessment
What to Expect
A 90-minute comprehensive assessment where you design an end-to-end solution for a realistic data engineering problem. You'll receive a business scenario (e.g., 'Build a data pipeline for real-time analytics serving a mobile app with millions of users' or 'Design a data warehouse for a multi-country logistics company'). You must: clarify requirements, propose architecture, justify technology choices, outline implementation approach, discuss trade-offs, and address non-functional requirements. This may be a live design session or take-home project completed before the interview. You should provide clear documentation: architecture diagrams, technology justifications, trade-off analysis, and implementation roadmap. The interviewer assesses holistic problem-solving, informed decision-making under constraints, communication clarity, and engineering maturity. This round validates that you can apply all skills learned in previous rounds to deliver cohesive, production-ready solutions.
Tips & Advice
Treat this like a real project you'd own. Start by asking clarifying questions: data volume and growth trajectory? Required latency? Key business metrics and use cases? Current systems and constraints? Team size and expertise? Budget constraints? Timeline? Create a clear architecture diagram showing all components (sources, ingestion, storage, processing, consumption) and data flow. For each technology choice, provide explicit reasoning: 'We chose Kafka for streaming because [specific reason]; S3 for data lake because [specific reason]; Spark for processing because [specific reason].' Document trade-offs at each decision point: why Kafka over Pub/Sub? Why Spark over Flink? Acknowledge that different choices fit different constraints. Include non-functional considerations: How does it scale 5x, 10x? How do you handle failures? What monitoring and alerts are needed? How do you ensure data quality? What about security and compliance? Outline implementation phases and team structure. For take-home projects, submit well-documented work with diagrams, clear writing, and thoughtful analysis. In live discussions, be prepared to defend choices and pivot if challenged. Reference patterns and lessons from actual projects. Show maturity by acknowledging limitations, discussing what you'd do differently with more resources, and planning for monitoring/observability from the start.
Focus Topics
Implementation Planning and Execution Strategy
Creating actionable implementation plans with phases, dependencies, and resource requirements. Identifying risks and mitigation strategies. Outlining development approach, testing strategy, and deployment plan. Understanding project management and cross-functional collaboration.
Practice Interview
Study Questions
Communication and Technical Documentation
Clearly communicating complex technical ideas through diagrams (architecture diagrams, data flow diagrams, ER diagrams), written documentation, and verbal explanation. Making designs easy for others to understand, build upon, and maintain.
Practice Interview
Study Questions
Technical Trade-off Analysis and Decision Making
Ability to weigh multiple options (batch vs. real-time, managed vs. self-managed, vendor vs. open-source, cost vs. flexibility) and make informed decisions grounded in requirements. Explicitly acknowledging trade-offs rather than pretending solutions are perfect.
Practice Interview
Study Questions
End-to-End Data Pipeline Design
Designing complete data solutions from problem statement through implementation and operations. Integrating data ingestion, transformation, storage, and consumption layers. Considering scalability, reliability, maintainability, and operational simplicity holistically. Making coherent architectural choices that work together.
Practice Interview
Study Questions
Behavioral and Leadership Interview
What to Expect
A 60-minute interview assessing cultural fit, collaboration, and leadership principles. You'll discuss past experiences using the STAR method (Situation, Task, Action, Result). Expect questions like: 'Tell about a time you resolved conflict with a team member,' 'Describe a project failure and what you learned,' 'Give an example of mentoring a junior engineer,' 'How do you handle ambiguity?' The interviewer evaluates whether you embody FAANG leadership principles, work effectively in ambiguous environments, communicate across teams, demonstrate growth mindset, and have mentorship potential. For mid-level positions, expect emphasis on owning projects, contributing to team culture, and developing others—not executive-level leadership.
Tips & Advice
Prepare 6-8 compelling stories covering diverse themes: overcoming technical challenges, learning from failure, teamwork and collaboration, mentorship and developing others, owning projects and driving impact, conflict resolution, handling ambiguity, and customer focus. Use the STAR method: describe the Situation and your role, explain the Task, detail specific Actions you took, and quantify Results. Practice delivering stories in 2-3 minutes each. Tailor stories to FAANG principles if possible: Amazon (Customer Obsession, Ownership, Invent and Simplify, Think Big), Google (Collaboration, Integrity, Excellence), Meta (Move Fast, Focus on Impact, Be Bold). Be authentic—interviewers detect rehearsed or exaggerated responses. Show self-awareness by discussing mistakes honestly and what you learned. Highlight collaborative examples showing how you worked with others to achieve goals. For mid-level position, emphasize mentorship with concrete examples: helped junior engineer grow from [initial capability] to [improved capability], led onboarding for new team member, etc. Prepare thoughtful questions about team culture, technical direction, growth opportunities, and how they support engineer development. Practice with a friend or mock interviewer to get feedback on delivery and authenticity.
Focus Topics
Mentorship and Developing Team Members
Examples of helping junior engineers and team members grow. Concrete instances of mentoring, knowledge sharing, constructive feedback, and creating learning opportunities. Demonstrating investment in others' success and team capability development.
Practice Interview
Study Questions
Ownership and Impact Orientation
Taking ownership of problems and projects, driving them to completion, and measuring impact. Examples of going beyond requirements to deliver value, solving problems proactively, and owning outcomes rather than just tasks.
Practice Interview
Study Questions
Handling Ambiguity and Learning Agility
Comfort with unclear situations and incomplete information. Ability to ask the right questions, break down ambiguity, and move forward. Examples of learning new technologies or domains quickly, adapting to change, learning from failures, and growth mindset.
Practice Interview
Study Questions
Teamwork and Cross-Functional Collaboration
Ability to work effectively with diverse team members (data scientists, analysts, platform engineers), communicate clearly, and contribute to collective success. Examples of supporting colleagues, asking for help when needed, and sharing knowledge generously. Demonstrating collaborative rather than individualistic approach.
Practice Interview
Study Questions
FAANG Leadership Principles and Cultural Values
Understanding and embodying company-specific leadership principles and values. For Amazon: Customer Obsession, Ownership, Invent and Simplify, etc. Aligning experiences and communication with these principles. Demonstrating how your approach reflects company values through concrete examples.
Practice Interview
Study Questions
Hiring Manager Round
What to Expect
A final 45-minute conversation with the hiring manager or engineering lead for the team. This round is less adversarial than previous technical rounds; it focuses on verification of technical depth, team fit, and long-term potential. The hiring manager will discuss your technical qualifications, potentially dive deeper into 1-2 areas to verify expertise, discuss career trajectory and growth expectations, and explore how you'd work with their specific team. Expect: 'Walk me through your most impressive technical achievement,' 'Where do you want to be in 5 years?' 'What technical areas excite you?' 'How do you stay current with the field?' You'll also have opportunity to assess the team: ask about team challenges, technical direction, culture, and mentorship opportunities. The hiring manager determines if you can succeed in their specific team and environment.
Tips & Advice
Treat this as a genuine conversation rather than an interrogation. Come prepared to discuss your most significant technical contribution with genuine enthusiasm, technical depth, and personal reflection. Explain challenging problems you solved, the lessons learned, and how that experience shapes your approach. Have a clear narrative about your career progression and future trajectory: where you want to be in 2-3 years, what skills you want to develop, whether you're interested in management or technical expertise path. Be authentic about your strengths and areas for growth—hiring managers respect self-awareness. Ask thoughtful questions demonstrating genuine interest in their problems: 'What are the biggest technical challenges your team faces?' 'How does your team approach balancing shipping features with technical infrastructure?' 'What does the team culture value?' 'How do you support engineer growth and learning?' Show interest in their problems and how you'd approach them. If you have concerns about the role or team, raise them now—this is your opportunity to assess fit. Reference positive conversations with other interviewers if relevant. Discuss how you'd approach working with data scientists, analysts, and product teams. End by expressing genuine interest and asking about next steps.
Focus Topics
Career Growth and Role Alignment
Clear vision for career development, technical areas you want to deepen, and how this role aligns with your goals. Demonstrating thoughtfulness about your trajectory—whether you're interested in deep technical expertise, technical leadership, or other paths.
Practice Interview
Study Questions
Team Dynamics and Cultural Fit
Ability to work effectively with the specific team, understanding their composition, challenges, and culture. Asking intelligent questions about team problems and how you'd contribute. Demonstrating genuine interest in team success, not just personal advancement.
Practice Interview
Study Questions
Technical Depth Verification
Deep exploration of 1-2 areas from your background to verify claimed expertise. Discussing real-world challenges you've faced, how you approached them, and technical trade-offs. Demonstrating genuine mastery of claimed skills, not just surface-level knowledge.
Practice Interview
Study Questions
Frequently Asked Data Engineer Interview Questions
Sample Answer
import org.apache.spark.SparkConf
val conf = new SparkConf()
.setAppName("MyApp")
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
.set("spark.kryo.registrator", "com.example.MyKryoRegistrator")
.set("spark.kryo.registrationRequired", "true") // optional but enforces registrationimport com.esotericsoftware.kryo.Kryo
import org.apache.spark.serializer.KryoRegistrator
class MyKryoRegistrator extends KryoRegistrator {
override def registerClasses(kryo: Kryo) {
kryo.register(classOf[com.myapp.MyRecord])
kryo.register(classOf[org.some.lib.CustomType])
}
}spark-submit \
--conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
--conf spark.kryo.registrator=com.example.MyKryoRegistrator \
--conf spark.kryo.registrationRequired=true \
--class com.example.Main myapp.jarSample Answer
-- merge staging into target using natural key to make sink idempotent
MERGE INTO target_table t
USING staging_table s
ON t.natural_key = s.natural_key
WHEN MATCHED THEN
UPDATE SET t.col1 = s.col1, t.updated_at = s.updated_at
WHEN NOT MATCHED THEN
INSERT (natural_key, col1, updated_at) VALUES (s.natural_key, s.col1, s.updated_at);-- dedupe staging first, then insert ignoring existing keys
WITH dedup AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY natural_key ORDER BY event_time DESC) rn
FROM raw_events
)
INSERT INTO target_table (natural_key, col1, event_time)
SELECT natural_key, col1, event_time
FROM dedup WHERE rn = 1
ON CONFLICT (natural_key) DO NOTHING;Sample Answer
Sample Answer
Sample Answer
Sample Answer
val deduped = df
.withWatermark("event_time","10 minutes")
.dropDuplicates(Seq("event_id"))df.write
.option("path", s"s3://bucket/staging/batch=$batchId")
.save()
// then move to final and record batchId in a committed-log (S3/DB)Sample Answer
SELECT
service,
event_time::date AS day,
percentile_cont(0.95) WITHIN GROUP (ORDER BY response_time)
OVER (
PARTITION BY service
ORDER BY event_time
RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW
) AS p95_7d
FROM events
WHERE event_time >= NOW() - INTERVAL '90 days'; -- limit scanning-- 1) pre-aggregate daily tdigests per service
CREATE TABLE daily_tdigests AS
SELECT service, date(event_time) AS day, tdigest_agg(response_time) AS td
FROM events
GROUP BY service, date(event_time);
-- 2) for each day, merge last 7 days' tdigests and extract p95
SELECT d.service, d.day,
tdigest_quantile(tdigest_merge(array_agg(dt.td)), 0.95) AS p95_7d
FROM daily_tdigests d
JOIN daily_tdigests dt
ON dt.service = d.service
AND dt.day BETWEEN d.day - INTERVAL '6 days' AND d.day
GROUP BY d.service, d.day;Sample Answer
Sample Answer
Sample Answer
Recommended Additional Resources
- LeetCode - Practice coding problems, focus on medium-difficulty data manipulation problems. Tag: Data Engineers
- Mode Analytics SQL Tutorial - Comprehensive SQL practice with real datasets for data analysis and optimization
- Cracking the Coding Interview by Gayle Laakmann McDowell - Classic guide with detailed explanations for technical interviews
- System Design Primer - Excellent resource for understanding distributed systems concepts and architectural trade-offs
- Apache Spark Documentation - Official Spark documentation covering architecture, APIs, and optimization
- Designing Data-Intensive Applications by Martin Kleppmann - Deep dive into distributed systems, consistency models, and data design patterns
- AWS Data Services Documentation - Official AWS guides for Redshift, S3, EMR, Glue, and Kinesis
- Azure Synapse and Data Lake Documentation - Official Azure guides for data warehousing and data lake solutions
- Google Cloud BigQuery Documentation - Official GCP guides for BigQuery, Dataflow, and Cloud Storage
- DataCamp Courses - Structured courses on Python, SQL, Spark, and cloud data engineering
- InterviewQuery - Data engineering specific interview preparation platform with realistic questions
- Blind Coding Interview Platform - Practice mock interviews with candidates and receive feedback
- YouTube Channels: DataTalks.Club, Seattle Data Guy, Seattle Data Engineer - Real-world data engineering architecture discussions
- Medium Articles on Data Engineering - Search for data pipeline design, ETL patterns, and architectural discussions
- DBDesigner.net - Online tool for designing and practicing database schemas
- HackerRank SQL and Python - Additional practice platform for coding and SQL problems
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