Meta Data Engineer Interview Preparation Guide - Junior Level
Meta's Data Engineer interview process for junior-level candidates consists of a structured pipeline designed to assess SQL fundamentals, Python programming, data modeling capabilities, and product thinking. The process includes an initial recruiter screening, two technical phone screens (SQL and Python), and four onsite interview rounds covering product sense, data modeling, system design, and advanced technical skills. The entire process typically spans 4-6 weeks from application to offer.
Interview Rounds
Recruiter Screening
What to Expect
This is your first conversation with the Meta recruiter. The focus is on understanding your background, career trajectory, motivation for joining Meta, and basic fit for the role. This round typically happens over the phone or video call and serves as a filtering step to ensure you meet the baseline requirements. The recruiter will also explain the interview process and answer any logistical questions. This is your chance to demonstrate enthusiasm for the role and the company, and to ensure your experience aligns with what they're looking for.
Tips & Advice
Be specific about your data engineering experience and highlight projects where you built data pipelines or handled data infrastructure. Clearly articulate why you're interested in Meta specifically - mention a product or initiative if possible. Prepare 2-3 concrete examples from your past work that demonstrate your capabilities. Ask thoughtful questions about the team, the data stack they use, and what success looks like in the first 90 days. Be honest about your experience level as a junior engineer - recruiters appreciate self-awareness and humility. Keep your answers concise but substantive. Have your resume and project details readily available for reference during the call.
Focus Topics
Communication & Team Collaboration
Discuss how you communicate with teammates, especially cross-functional partners like data scientists, analysts, or product managers. Share an example of how you clarified requirements, explained technical constraints to non-technical stakeholders, or worked with diverse teams to deliver value. Describe your approach to working in teams and how you handle feedback.
Practice Interview
Study Questions
Learning & Growth Mindset
As a junior engineer, emphasize your ability and enthusiasm to learn new technologies and best practices. Share examples of how you've picked up new skills quickly, adapted to unfamiliar challenges, or learned from more experienced engineers. Demonstrate curiosity about data systems, engineering practices, and emerging technologies in data infrastructure.
Practice Interview
Study Questions
Motivation for Meta & Data Engineering Role
Explain why you're interested in working as a data engineer at Meta specifically. Discuss what attracts you to the company, the role, or the team. Reference Meta products like Facebook, Instagram, or Reels, or specific infrastructure challenges Meta solves. Show genuine interest rather than generic enthusiasm. Discuss why data engineering appeals to you beyond just career progression.
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Study Questions
Professional Background & Data Engineering Experience
Discuss your data engineering experience, projects you've worked on, technologies you've used, and specific accomplishments. Focus on concrete examples of data pipelines, ETL processes, or data infrastructure you've built. Even as a junior, highlight projects where you took ownership, solved problems, or improved processes. Be prepared to discuss your technical skills: SQL, Python, data platforms you've used.
Practice Interview
Study Questions
SQL Technical Screen
What to Expect
This is the first technical phone interview focused entirely on SQL. You'll be given real-world data scenarios and asked to write SQL queries to answer business questions. Questions typically involve analyzing sales data, user behavior, transactions, or content metrics. You'll be working in a shared document or coding platform where the interviewer can see your code in real-time. The goal is to assess your understanding of SQL fundamentals, your ability to reason through data problems, and how you handle edge cases. For junior-level candidates, expect queries involving joins, aggregations, filtering, GROUP BY, subqueries, and CTEs, but not highly complex nested queries or advanced window functions.
Tips & Advice
Start by clarifying the data schema and business problem before writing code. Ask questions about the data if the problem is ambiguous. Write readable SQL with proper formatting and comments explaining your logic. Test your queries mentally for edge cases (NULL values, duplicates, boundary conditions). If you get stuck, talk through your approach rather than staying silent - interviewers want to understand your problem-solving process. Practice writing queries without looking up syntax; you should be comfortable with joins, aggregations, subqueries, and CTEs. Write optimal queries when possible but prioritize correctness over performance for junior-level expectations. After writing your query, explain what it does and walk through an example with sample data. Don't memorize query templates; instead, understand the underlying concepts so you can adapt to novel problems.
Focus Topics
Handling NULL Values & Edge Cases
Understand how NULL values behave in queries (in comparisons, aggregations, joins). Use COALESCE, IFNULL, or similar functions to handle NULLs appropriately. Think about duplicate records and how to handle them correctly. Consider boundary conditions (first/last day of data, zero values, empty result sets). Anticipate and handle edge cases without being asked by the interviewer.
Practice Interview
Study Questions
Complex Query Writing with Subqueries & CTEs
Master subqueries and Common Table Expressions (CTEs). Write queries that break down complex problems into smaller steps using CTEs (WITH clauses). Understand correlated subqueries and when to use them vs. joins. Practice using CASE statements for conditional logic. Combine multiple techniques in a single query to solve realistic multi-step problems.
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Study Questions
Query Optimization & Performance Awareness
Understand the concept of query execution and basic performance considerations. Learn to identify inefficient patterns (full table scans, unnecessary joins, N+1 problems). Practice writing equivalent queries in different ways and understanding which is more efficient. Understand basic indexing concepts and their impact. As a junior, focus on writing logical, reasonable queries rather than micro-optimizations, but be aware of major performance pitfalls.
Practice Interview
Study Questions
SQL Joins & Multi-table Query Fundamentals
Master INNER, LEFT, RIGHT, FULL, and CROSS joins. Understand when to use each type and how they differ in result sets. Practice writing multi-table joins to combine data from different sources. Understand the difference between filtering in WHERE vs. ON clauses. Work on queries that require joining 3+ tables to answer business questions. Understand join conditions and how to avoid Cartesian products.
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Study Questions
Aggregations & Data Analysis
Practice GROUP BY, HAVING, aggregate functions (SUM, COUNT, AVG, MIN, MAX, COUNT DISTINCT). Understand how to calculate business metrics like total sales, unique user counts, percentages, and averages. Work with multiple aggregation levels and filtering aggregated results with HAVING clauses. Practice using DISTINCT to handle duplicates. Understand when aggregations return NULL vs. 0.
Practice Interview
Study Questions
Python/Coding Technical Screen
What to Expect
This is the second technical phone interview, focused on Python programming and algorithm problem-solving. You'll be given 2-4 coding problems to solve during this 45-minute session. Problems are typically medium difficulty for junior candidates and might involve string/list manipulation, algorithm patterns (binary search, two pointers, etc.), or simple data structure operations. You'll write code in a shared editor and explain your approach. The interviewer is assessing your ability to think through problems systematically, write clean code, understand time/space complexity, and handle errors. You're expected to be comfortable with Python fundamentals and able to solve straightforward algorithmic problems.
Tips & Advice
Before writing code, ask clarifying questions and explain your approach out loud. Start with a brute force solution if needed, then optimize. Write readable code with meaningful variable names and comments. Test your solution mentally with 2-3 test cases, including edge cases (empty input, single element, boundary values). Explain the time and space complexity of your solution. If you make a mistake, catch it yourself and fix it - this shows attention to detail and debugging ability. At the junior level, correctness matters more than finding the absolute optimal solution. Practice on LeetCode or similar platforms, focusing on problems rated Easy to Medium (Level 1-2). Use Python features confidently (list comprehensions, built-in functions, string methods) but don't overcomplicate simple problems. Be comfortable with basic data structures: lists, dictionaries, sets, and strings.
Focus Topics
Error Handling & Code Quality
Write clean, readable code with proper variable naming (descriptive names, not single letters except for loop counters). Add comments where logic isn't immediately obvious. Handle edge cases gracefully (empty inputs, single elements, boundary conditions). Use try-except blocks appropriately when needed. Avoid redundant code; use functions and loops effectively. Follow Python conventions and idioms (PEP 8). Show that you write code you'd be comfortable sharing with a team.
Practice Interview
Study Questions
Time & Space Complexity Analysis
Understand Big O notation: O(1), O(n), O(n²), O(log n), O(n log n). Be able to analyze the time and space complexity of your solution. Recognize common complexity patterns and what causes them. Explain why your solution has a particular complexity. Understand tradeoffs between time and space, and when optimization matters. Communicate complexity clearly to the interviewer.
Practice Interview
Study Questions
Python Fundamentals (Lists, Dictionaries, Strings)
Master working with Python's core data structures. Understand list operations (append, insert, pop, slicing, iteration), dictionary operations (keys, values, get, setdefault, pop), and string manipulation (split, join, replace, formatting, slicing). Practice using list comprehensions and dictionary comprehensions for concise and readable code. Understand when to use each data structure for different problems. Be comfortable with built-in functions like len, min, max, sum, sorted.
Practice Interview
Study Questions
Algorithm Problem-Solving Patterns
Practice solving coding problems involving common patterns: two pointers, sliding window, searching (binary search), sorting, and simple recursion. Work on problems involving arrays, strings, and basic graph traversal. Understand how to break down a problem into smaller steps and implement solutions that handle edge cases. Practice problems from meta/facebook interview question banks.
Practice Interview
Study Questions
Onsite Round 1: Product Sense & Business Impact
What to Expect
During this onsite round, you'll be evaluated on your ability to think like a product analyst and define meaningful metrics. The interviewer will present a product scenario (e.g., 'How would you measure the success of a new Instagram Reels feature?' or 'How would you check if Facebook should change something in the newsfeed?') and ask you to identify relevant metrics, KPIs, and how you'd track them. You might also be asked how you'd diagnose why a metric dropped or what data you'd collect for a new feature. This round assesses whether you understand the 'why' behind data - that data should drive business decisions. You'll be expected to think about the user experience, business goals, and technical feasibility. For junior-level candidates, you're not expected to have deep product knowledge, but you should demonstrate logical thinking about how to measure success.
Tips & Advice
Start by clarifying what success means for the product feature or metric. Ask about user segments, business objectives, and any constraints. Suggest 3-5 primary metrics (not dozens - quality over quantity). For each metric, explain what it measures and why it matters to the business. Think about causality: does this metric actually measure what we care about, or is it a proxy? Consider multiple dimensions: engagement, retention, quality, monetization, user experience, etc. Walk the interviewer through your thinking process rather than jumping to answers. Connect your metrics back to business impact - explain how each metric would influence product decisions. Be comfortable discussing tradeoffs: a metric might be easy to measure but might not reflect reality. At the junior level, depth of thinking matters more than perfect answers. Ask good follow-up questions. Avoid generic answers; be specific to the scenario presented and reference actual Meta products and features.
Focus Topics
Communicating Insights Clearly
Practice explaining complex metrics and trade-offs in simple language. Develop the ability to say 'I don't know, but here's how I'd find out' when stuck. Use specific examples to illustrate concepts. Engage with feedback; if the interviewer challenges your metric, understand their concern and adapt your thinking. Avoid unnecessary jargon. Walk through your logic step by step so interviewers understand your reasoning.
Practice Interview
Study Questions
Data-Driven Decision Making & Diagnostics
Practice thinking about how data informs product decisions. Understand A/B testing concepts and how experiments validate hypotheses. Learn to connect metrics to user experience improvements. Think about leading vs. lagging indicators. Practice identifying what data you'd need to answer business questions. Understand how to diagnose problems (e.g., if monthly active users dropped, what could have caused it and what data would you check? What would you visualize?).
Practice Interview
Study Questions
Metric Definition & KPI Selection
Learn to identify relevant metrics for business problems. Understand the difference between metrics (measurements), KPIs (key performance indicators), and vanity metrics. Practice defining metrics that are: meaningful (reflect business value), actionable (can influence decisions), and measurable (can be tracked in systems). Work on selecting the 3-5 most important metrics for different scenarios. Understand leading vs. lagging indicators.
Practice Interview
Study Questions
Meta Product Understanding & User Behavior
Familiarize yourself with Meta's major products and features: Facebook (News Feed, Engagement, Ads), Instagram (Stories, Reels, Explore, Feed), Messenger, WhatsApp. Understand how these products generate value and what metrics matter for each. Think about user journeys and key moments of engagement. Understand Meta's business model: advertising revenue, user engagement, retention. Review Meta's earnings calls or investor presentations to understand what the company prioritizes.
Practice Interview
Study Questions
Onsite Round 2: Data Modeling & Schema Design
What to Expect
This round focuses on your ability to design data structures and schemas that enable analytics and data retrieval at scale. You'll be given a business scenario (e.g., 'Design a database for an app like Google Classroom' or 'Design a relational database for Uber') and asked to design tables, define relationships, and potentially write SQL queries against your design. You might be asked to normalize or denormalize based on use cases, discuss partitioning strategies, or optimize for specific query patterns. The interviewer wants to see that you understand how data should be organized to support analytics efficiently. You'll likely sketch out your schema, explain your reasoning, discuss trade-offs, and potentially write some SQL queries to retrieve data from your design.
Tips & Advice
Start by clarifying the business requirements and use cases. Ask who the users are and what queries they'll run - this drives your design decisions. Draw out your schema on a whiteboard or shared doc - don't just describe it verbally. For each table, explain what it represents, what data it stores, and why. Define primary keys, foreign keys, and relationships clearly. Discuss normalization trade-offs: normalized schemas are good for data integrity but require more joins; denormalized schemas are faster for reads but risk data redundancy. At the junior level, a reasonable schema with clear thinking is better than a complex design. Consider data volume and query patterns when deciding on structure. Use familiar patterns like star schemas for analytics. Be ready to write a few example queries against your design to demonstrate that it works. Discuss indexing strategy and partitioning if appropriate. Ask clarifying questions if something is unclear rather than making assumptions.
Focus Topics
Scaling & Partitioning Strategies
Understand partitioning strategies: by date, by user segment, by geography, by product, etc. Think about how to partition large tables for efficient querying and data management. Consider how partitioning affects data ingestion speed and query performance. Discuss bucketing strategies if relevant. Understand how partitioning helps with data lifecycle management (retention, archival).
Practice Interview
Study Questions
Handling Real-World Data Complexity
Think about real-world data challenges: late-arriving data, data corrections and updates, slowly changing dimensions, handling deletes, data quality issues, and historical tracking. Design schemas and processes that handle these gracefully. Understand SCD (Slowly Changing Dimensions) Type 1, 2, and 3 patterns. Think about how your design supports these scenarios.
Practice Interview
Study Questions
Data Mart Design for Analytics
Learn to design data marts that serve specific analytical needs. Understand slowly changing dimensions (handling updates to dimension tables over time). Practice designing for common analytics patterns: time-series analysis, cohort analysis, funnel analysis, segmentation. Design tables that make common queries efficient. Think about how to support analytics dashboards and reports.
Practice Interview
Study Questions
Normalization vs. Denormalization Trade-offs
Understand the tradeoffs between normalized and denormalized schemas. Normalized schemas reduce data redundancy, maintain consistency, and prevent anomalies but require joins. Denormalized schemas are faster for reads but require careful maintenance to avoid inconsistency. Learn when to choose each approach based on query patterns and data volume. Practice identifying opportunities for strategic denormalization when it makes sense for analytics use cases.
Practice Interview
Study Questions
Schema Design & Star Schema Patterns
Understand how to design database schemas for analytics. Learn the star schema pattern: fact tables (events, transactions, impressions) surrounded by dimension tables (users, products, dates, campaigns). Practice designing schemas for different business domains. Understand the distinction between fact and dimension tables and why they're useful for analytics. Learn basic normalization forms and when full normalization is appropriate vs. when strategic denormalization helps. Understand slowly changing dimensions.
Practice Interview
Study Questions
Onsite Round 3: System Design - Data Pipelines & ETL
What to Expect
This round evaluates your ability to design end-to-end data pipelines and ETL (Extract, Transform, Load) processes. You'll be given a business scenario or data challenge and asked to design how data flows from source systems through transformation to final storage and accessibility. Example scenarios: 'Design a data platform to compute engagement metrics for Reels in near real-time' or 'Architect a system that logs, stores, and surfaces ad-performance data to multiple downstream consumers.' You might discuss data ingestion patterns, transformation logic, failure handling and retries, monitoring, and ensuring data quality. The interviewer wants to understand your systems thinking: how would you handle scale, what technologies would you use, what are the tradeoffs between batch and streaming, how do you ensure reliability. For junior-level candidates, expect more structured guidance and hints; you're not expected to independently arrive at complex distributed systems, but you should show solid understanding of fundamental pipeline concepts.
Tips & Advice
Start by understanding the requirements: What data needs to flow? How fresh should it be (real-time vs. daily)? What's the data volume and velocity? What are the SLAs? Then sketch out the high-level flow: source systems → data ingestion → transformation → storage → analytics layer. Discuss technology choices and your reasoning: would batch processing suffice or do you need streaming? Hadoop/Spark for transformation? Kafka for ingestion? Airflow for orchestration? Think through failure scenarios: what if data ingestion fails? What if transformation takes longer than expected? What if the destination is unavailable? Design monitoring and alerting. At the junior level, show that you understand these concepts and can discuss trade-offs intelligently, even if you don't have extensive hands-on experience with every technology. Be comfortable discussing tools like Airflow for orchestration and the concept of DAGs. Think about data quality: how would you validate data at each stage? What checks would catch problems? Explain your architecture clearly but don't overcomplicate it - a simple, well-reasoned design is better than a complex one you can't fully explain. Be ready to dive deeper into specific components if asked.
Focus Topics
Failure Handling, Retries & Data Consistency
Think through failure scenarios: source system is down, network issues, target storage full, transformation errors, data arriving late. Design pipeline logic that handles these gracefully. Understand idempotency: how do you ensure that rerunning a pipeline produces the same result? Learn about exactly-once delivery, at-least-once delivery, and their implications. Discuss alerting: how would you know if the pipeline failed? What actions would be triggered?
Practice Interview
Study Questions
Pipeline Orchestration & Scheduling (Airflow)
Understand the concept of DAGs (Directed Acyclic Graphs) and pipeline orchestration. Learn about Airflow at a conceptual level: how tasks depend on each other, how to schedule pipelines, how to handle retries and failure handling. Understand the difference between batch scheduling (run daily at 2am) and event-triggered or continuous pipelines. Think about task dependencies and how they affect pipeline efficiency.
Practice Interview
Study Questions
Data Transformation & Quality Checks
Understand how to clean and transform raw data: handling missing values and NULLs, removing duplicates, validating data types, casting between types, applying business logic, aggregations, joins, enrichment. Design data quality checks to catch problems early: row count validation, schema validation, value range checks, referential integrity checks, freshness checks. Learn how to document data quality expectations and alert when they're violated. Understand what data quality means at different stages of the pipeline.
Practice Interview
Study Questions
ETL Pipeline Design Fundamentals
Understand the basic ETL pattern: Extract (pull data from sources), Transform (clean, aggregate, join, enrich), Load (store in data warehouse/lake). Understand different ETL architectures: batch vs. streaming, traditional ETL vs. ELT (where loading happens before transformation). Learn when to use each approach and the trade-offs. Practice thinking through the steps needed to move data from various sources into usable analytics tables.
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Study Questions
Data Ingestion Patterns & Technologies
Learn different data ingestion approaches: REST APIs, database change data capture (CDC), log ingestion, batch file uploads, event streaming. Understand the pros and cons of each. Familiarize yourself with tools: Kafka for streaming, Apache Beam, custom ingestion services, cloud-native solutions. Understand pull vs. push architectures. At junior level, you should know these options exist, understand their general purposes, and be able to discuss trade-offs.
Practice Interview
Study Questions
Onsite Round 4: Advanced Technical & Collaboration
What to Expect
This final onsite round brings together multiple focus areas and includes both technical and behavioral assessment. It typically involves advanced SQL or data architecture questions, and also evaluates how you collaborate, communicate, handle ambiguity, and learn. You might face complex SQL queries, design another data system, or discuss a challenging project from your past. The interviewer will also assess your ability to take feedback constructively, communicate clearly about technical trade-offs, and show genuine interest in Meta's problems and culture. This round often includes meeting a potential manager or team member. It confirms that you're technically capable, culturally aligned with Meta's values (ownership, focus, collaboration), and someone the team would want to work with.
Tips & Advice
Treat this as both a technical and behavioral round. For technical portions, apply everything you've learned - show clean thinking, ask clarifying questions, explain trade-offs thoughtfully. For behavioral portions, use concrete STAR examples from your experience. Be authentic: discuss challenges you've faced, mistakes you've made, and what you learned from them. Show curiosity about the team's work, their infrastructure challenges, and how you could contribute. Ask thoughtful questions about the data infrastructure, team structure, scaling challenges, and growth opportunities. At the junior level, showing learning ability and coachability is just as important as technical skills. Demonstrate intellectual humility: it's okay to say 'I don't know, but here's how I'd figure it out.' Be enthusiastic about Meta's scale and data challenges. Avoid overconfidence or defensive reactions to feedback. Remember that interviewers are also evaluating whether they want to work with you and whether you'll thrive in their team environment.
Focus Topics
Incremental Loads & Change Data Capture (CDC)
Understand incremental data loading: instead of reprocessing all data, process only what changed. Learn change data capture (CDC) concepts and patterns. Understand how to track what's new or changed since the last load. Discuss when full loads vs. incremental loads make sense. Practice designing incremental load logic.
Practice Interview
Study Questions
Data Quality Implementation & Monitoring
Understand how to implement robust data quality checks in pipelines. Learn about anomaly detection techniques. Understand data validation frameworks and quality scoring. Discuss how to catch data issues early and alert teams appropriately. Understand SLAs for data freshness and quality. Learn how to document data quality expectations and monitor actual quality.
Practice Interview
Study Questions
Query Performance Optimization & Execution Plans
Understand how to profile query performance and identify bottlenecks. Learn to read query execution plans and understand what they tell you about performance. Understand indexing strategies and their trade-offs. Discuss partitioning and clustering strategies for large tables. Practice identifying when a query is inefficient and how to rewrite it. Understand the difference between premature optimization and necessary performance work.
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Study Questions
Complex SQL & Window Function Techniques
Master advanced SQL concepts: window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, running aggregates), recursive CTEs, percentile calculations. Practice queries that combine multiple advanced techniques. Work on time-series analysis queries, retention analysis, cohort analysis. Solve problems that require sophisticated data manipulation and analysis.
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Study Questions
Behavioral: Ownership, Collaboration & Continuous Learning
Prepare examples showing: 1) Ownership - taking charge of problems even outside your assigned scope, being proactive, following through, 2) Collaboration - working effectively with teams across engineering, analytics, product, 3) Learning - picking up new skills quickly, adapting to feedback, improving over time, learning from mistakes. Reflect on mistakes you've made and what you learned. Discuss how you handle ambiguity or unclear requirements. Prepare questions showing genuine interest in Meta's data challenges, team culture, and engineering values.
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Frequently Asked Data Engineer Interview Questions
Sample Answer
Sample Answer
Sample 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
WITH first_tx AS (
SELECT
t.customer_id,
MIN(t.occurred_at) AS first_tx_at
FROM transactions t
GROUP BY t.customer_id
),
spend_30d AS (
SELECT
c.customer_id,
c.created_at,
c.country,
f.first_tx_at,
SUM(t.amount) AS total_spend_30d
FROM first_tx f
JOIN customers c ON c.customer_id = f.customer_id
JOIN transactions t
ON t.customer_id = f.customer_id
AND t.occurred_at >= f.first_tx_at
AND t.occurred_at < f.first_tx_at + INTERVAL '30' DAY
WHERE f.first_tx_at >= CURRENT_TIMESTAMP - INTERVAL '30' DAY
GROUP BY c.customer_id, c.created_at, c.country, f.first_tx_at
)
SELECT *
FROM spend_30d
ORDER BY first_tx_at DESC;Sample Answer
WITH events_with_row AS (
SELECT *,
ROW_NUMBER() OVER (PARTITION BY event_id ORDER BY event_time DESC) AS rn
FROM raw_events
WHERE event_time >= window_start AND event_time < window_end
)
SELECT * EXCEPT(rn)
FROM events_with_row
WHERE rn = 1;Sample Answer
import random
def quickselect(arr, k):
"""Return kth largest (k is 1-based)."""
target = len(arr) - k
def median_of_three(a, l, r):
m = (l + r) // 2
trio = [(a[l], l), (a[m], m), (a[r], r)]
return sorted(trio)[1][1]
def partition(l, r):
p_idx = median_of_three(arr, l, r)
arr[p_idx], arr[r] = arr[r], arr[p_idx]
pivot = arr[r]
i = l
for j in range(l, r):
if arr[j] <= pivot:
arr[i], arr[j] = arr[j], arr[i]
i += 1
arr[i], arr[r] = arr[r], arr[i]
return i
l, r = 0, len(arr) - 1
while l <= r:
p = partition(l, r)
if p == target:
return arr[p]
elif p < target:
l = p + 1
else:
r = p - 1
return NoneSample Answer
import time
import psycopg2
from psycopg2.extras import RealDictCursor
from psycopg2 import OperationalError, DatabaseError
MAX_RETRIES = 5
BACKOFF_BASE = 0.05 # seconds
def apply_idempotent(conn, idempotency_key, business_payload):
"""
Attempts an idempotent business write.
Returns True if the business write was applied by this call, False if another caller already applied it.
conn: a psycopg2 connection (autocommit must be False)
"""
for attempt in range(1, MAX_RETRIES + 1):
try:
with conn:
with conn.cursor() as cur:
# 1) Try to reserve the idempotency key. Using ON CONFLICT DO NOTHING is safe under concurrency.
cur.execute(
"""
INSERT INTO dedupe (idempotency_key, created_at)
VALUES (%s, now())
ON CONFLICT (idempotency_key) DO NOTHING
RETURNING id;
""",
(idempotency_key,)
)
row = cur.fetchone()
if row:
# We reserved the key - perform business write within the same transaction.
cur.execute(
"""
INSERT INTO business_table (col1, col2, created_at)
VALUES (%s, %s, now())
""",
(business_payload['col1'], business_payload['col2'])
)
return True
else:
# Key already present: idempotent - nothing to do.
return False
except (OperationalError, DatabaseError) as e:
# Detect transient serialization/deadlock errors (Postgres uses SQLSTATE codes)
sqlstate = getattr(e, 'pgcode', None)
transient = sqlstate in ('40001', '40P01') or isinstance(e, OperationalError)
if not transient or attempt == MAX_RETRIES:
raise
time.sleep(BACKOFF_BASE * (2 ** (attempt - 1)))
continueSample Answer
Recommended Additional Resources
- LeetCode - Medium level Python problems and SQL problem sets (focus on Meta interview questions)
- Prepfully - Data Engineer Interview Prep with Meta-specific guidance
- Interview Query - Data Engineering Interview Guide with real Meta questions
- DataInterview.com - Meta Data Engineer leaked questions
- Glassdoor - Meta Data Engineer interview reviews and questions from real candidates
- SQL practice platforms: HackerRank SQL, Mode Analytics SQL Tutorial, DataCamp SQL
- System Design Primer on GitHub - distributed systems concepts
- Apache Airflow documentation and tutorials
- Meta Engineering Blog - insights into Meta's data infrastructure and challenges
- Designing Data-Intensive Applications by Martin Kleppmann - reference book on distributed systems
- SQL Cookbook by Anthony Molinaro - practical SQL patterns and solutions
- High Growth Handbook by Elad Gil - understanding scaling and data at scale
- Official Meta Careers Page - latest job postings and company information
- LinkedIn - research Meta employees in data engineering roles and their career paths
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