Airbnb Staff Data Engineer Interview Preparation Guide
Airbnb's Staff Data Engineer interview is a rigorous, multi-stage process designed to assess expert-level technical depth in data systems, architectural thinking, system design capabilities, and leadership maturity. The process spans 3-5 weeks and includes a recruiter screening, technical phone screen, and a comprehensive 5-7 round onsite loop. The company emphasizes technical rigor combined with cultural alignment, collaboration, and data-driven problem-solving. At Staff level, candidates must demonstrate not only mastery of data engineering fundamentals but also the ability to architect complex systems, mentor team members, and influence technical strategy across the organization.
Interview Rounds
Recruiter Screening
What to Expect
A 30-minute initial conversation with Airbnb's recruiting team that sets the foundation for your interview journey. The recruiter will review your professional background, understand your motivations for the Staff-level role, and assess initial cultural fit. They may ask technical questions about your experience with specific data technologies, how you approach system optimization, or how you've grown your technical expertise over your career. This is your opportunity to deliver a compelling narrative highlighting your technical stack, recent major accomplishments, and what attracts you to the Data Engineering challenge at Airbnb. The recruiter will explore your most impactful projects, your leadership approach, and how your experience aligns with Airbnb's data infrastructure challenges at scale.
Tips & Advice
Research Airbnb's engineering culture, mission, and data challenges before the call. Prepare a compelling 60-second introduction covering your technical expertise, recent major accomplishments, and what excites you about solving Airbnb's data problems. Frame projects in terms of business impact, scale, and your specific leadership contributions—not just technical execution. Be ready to discuss why you're pursuing a Staff-level role specifically at Airbnb, and connect your career trajectory to the role's expectations. Show authentic enthusiasm for data engineering problems, not just compensation or prestige. The recruiter may ask technical questions like 'How do you approach optimizing a slow data pipeline?' or 'Walk me through your philosophy on data architecture.' Keep answers thoughtful but substantive. Demonstrate collaborative mindset and genuine passion for solving complex challenges.
Focus Topics
Technical Stack and Expertise Depth
Be fluent discussing your hands-on experience with distributed data frameworks (Apache Spark, Hadoop), cloud platforms (AWS, GCP, Azure), data warehouse/lake technologies, ETL orchestration, and monitoring systems. Highlight expertise with real-time and batch processing, data quality frameworks, and specific architectural decisions you've made. Be specific about versions, trade-offs, and why you chose certain technologies for specific problems.
Practice Interview
Study Questions
Impact-Driven Project Storytelling with Business Context
Prepare 3-4 compelling stories from your career highlighting large-scale, high-impact data projects. Structure each narrative: business challenge, your technical approach and leadership role, quantified outcomes (e.g., 50% latency reduction enabling real-time analytics for 200+ users, architecture supporting 10x data growth), and organizational learnings. Focus on projects demonstrating complexity, scale, mentorship, or strategic influence, not just technical competence.
Practice Interview
Study Questions
Airbnb Fit and Data Engineering Vision
Research Airbnb's data engineering challenges, platform scale, and technical culture. Understand the role of data in Airbnb's business model (booking platform, reviews, host/guest analytics). Articulate why Airbnb specifically appeals to you and how your expertise aligns with their data infrastructure challenges. Share your vision for data engineering's strategic value and how you'd contribute to it at Airbnb.
Practice Interview
Study Questions
Career Trajectory and Staff-Level Readiness
Articulate your professional journey from individual contributor to staff-level engineer. Highlight how you've progressively taken on more complex challenges, mentored others, and influenced technical direction. Demonstrate self-awareness about your strengths and growth areas. Connect your experience to staff-level responsibilities: technical leadership, cross-functional influence, strategic thinking, and mentorship of senior engineers.
Practice Interview
Study Questions
Technical Phone Screen
What to Expect
A 60-minute focused technical assessment delivered via HackerRank, CoderPad, or similar platform. You'll solve 1-2 moderately challenging problems involving data structures, algorithms, and/or complex SQL queries. Problems are grounded in real-world Airbnb scenarios—analyzing booking patterns, calculating user engagement metrics, optimizing data queries, or designing efficient ETL steps. The interviewer evaluates your problem-solving approach, code quality, edge case handling, and communication throughout the session. They assess whether you can handle the scale, complexity, and performance demands of Airbnb's production data systems. This round serves as a strong predictor of onsite technical performance and filters for candidates with strong fundamentals.
Tips & Advice
Practice extensively on HackerRank and LeetCode, focusing on medium to hard problems. For Staff level, expect problems requiring sophisticated data structure usage and algorithmic optimization. Start by clarifying the problem, asking about constraints and edge cases, then discuss your approach before coding. Write clean, well-commented, production-quality code with proper error handling. For SQL problems, optimize for performance—discuss indexing strategies, query plans, and window functions naturally. Test edge cases verbally before submitting. Your communication is as important as your solution. Explain your reasoning for design choices. If you hit an obstacle, discuss alternatives rather than struggling silently. At Staff level, interviewers expect you to recognize suboptimal solutions and proactively suggest improvements. Demonstrate that you think about performance, maintainability, and scalability.
Focus Topics
Problem Translation and Business Logic
Develop skill in translating ambiguous real-world problems (e.g., 'find top revenue hosts by city for last 30 days with minimum review count') into algorithmic problems. Ask clarifying questions about edge cases, data quality issues, and business definitions. Build solutions handling NULL data, duplicates, late arrivals, and timezone complexity.
Practice Interview
Study Questions
Python Data Manipulation and Functional Programming
Excel at list comprehensions, dict operations, functional programming patterns (map, filter, reduce), and libraries like Pandas/NumPy for data transformation. Handle edge cases robustly. Write idiomatic, performant Python code. Be comfortable with generators, decorators, and higher-order functions. At Staff level, your code should be both performant and readable, serving as a model for team members.
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Study Questions
Algorithm Optimization and Complexity Analysis
Develop strong intuition for algorithmic complexity (time and space). Practice identifying and optimizing O(n²) solutions to O(n log n) or O(n). Understand sorting algorithms, searching algorithms, and graph traversal patterns (DFS, BFS, Dijkstra, topological sort). Be able to articulate trade-offs between different approaches. At Staff level, recognize when brute-force solutions are acceptable versus when optimization is critical for scale.
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Study Questions
Advanced Data Structures in Python
Master data structures beyond basics: hash tables, heaps, trees (BST, balanced trees, Trie), graphs, and custom data structures. Understand time/space complexity tradeoffs and when to use each structure. Be comfortable implementing custom data structures when needed. Focus on practical applications: using heaps for top-K problems, graphs for dependency tracking in ETL DAGs, hash maps for efficient grouping/aggregation logic, Tries for prefix matching in data transformation.
Practice Interview
Study Questions
Advanced SQL for Data Processing and Optimization
Demonstrate mastery of window functions (PARTITION BY, ORDER BY, ROWS/RANGE frames, LAG/LEAD, ROW_NUMBER, RANK), complex joins (self-joins, multiple conditions, anti-joins), aggregations, subqueries, and CTEs. Write queries handling data lineage, calculating rolling metrics, and identifying anomalies. Optimize SQL for both readability and performance. Understand indexing strategies, query execution plans, and cost-based optimization. Think in sets rather than rows.
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Study Questions
Python and Spark Distributed Computing Round
What to Expect
A 60-minute onsite technical round where you'll solve a hands-on coding challenge using Python and Apache Spark. The problem typically involves distributed data processing—building a data processing job, handling fault tolerance, optimizing computation across a cluster, or implementing a complex data transformation. You'll work on provided code skeleton or start from scratch, demonstrating ability to write production-quality code at scale. The interviewer observes your problem-solving process, code organization, distributed systems thinking, and ability to reason about partitioning, shuffling, and performance optimization. At Staff level, you're expected to naturally consider code maintainability, testability, and scalability.
Tips & Advice
Familiarize yourself thoroughly with Apache Spark—RDD operations, DataFrame/Dataset APIs, lazy evaluation, partitioning strategies, caching, and shuffling. If pure Python is required, write modular, testable code with clear separation of concerns and appropriate design patterns. At Staff level, think naturally about code maintainability and scalability. Discuss trade-offs in your approach (memory vs. computation, partitioning strategies). Be prepared to optimize based on feedback. Show understanding of distributed systems concepts: data locality, serialization, memory management. If unfamiliar with specific Spark APIs, focus on demonstrating strong fundamentals—interviewers often prioritize learning ability and systems thinking over memorized syntax. Explain your reasoning for architectural decisions.
Focus Topics
Production-Quality Code and Testing
Write code with proper error handling, logging, and monitoring. Structure code into testable units. Implement data quality checks and assertions. Use configuration management. Write code that other engineers can understand and maintain. Consider edge cases and failure scenarios.
Practice Interview
Study Questions
Fault Tolerance and Idempotency
Understand Spark's fault tolerance model through lineage. Design processing logic that's idempotent—safe to replay without duplicate results. Handle exactly-once semantics for stateful operations. Implement checkpointing for long-running jobs. Discuss graceful error handling and recovery strategies.
Practice Interview
Study Questions
Distributed Data Processing and Partitioning Strategy
Understand data partitioning across cluster nodes, shuffle operations, and data locality. Design efficient partitioning strategies—too many partitions cause overhead, too few cause bottlenecks. Recognize when coalesce vs. repartition is appropriate. Design processing logic that minimizes shuffling. Understand how joins are executed in distributed systems.
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Study Questions
Performance Tuning and Resource Management
Recognize performance bottlenecks and optimize accordingly. Understand memory constraints, executor configuration, and parallelism. Use Spark's built-in tools—explain plans, Stage UI, event logs. Know when to cache intermediate results, when to adjust partition count, and when to use broadcast variables. Discuss memory management (heap, off-heap) and garbage collection implications.
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Study Questions
Apache Spark Architecture and RDD/DataFrame APIs
Understand Spark architecture: Driver, Executors, DAGs, tasks, stages, and shuffle. Master RDD transformations vs. actions and DataFrame APIs (select, filter, groupBy, join, window). Understand lazy evaluation and when actions trigger computation. Know when to use RDDs for unstructured data vs. DataFrames for structured data. Understand Spark SQL and catalyst optimizer. Be familiar with Spark Streaming for real-time processing.
Practice Interview
Study Questions
Advanced SQL and Data Manipulation Round
What to Expect
A 60-minute technical round focused on complex SQL and data manipulation challenges. You'll solve sophisticated queries involving multiple joins, window functions, aggregations, and data transformation logic. Problems are typically based on Airbnb's real data scenarios—analyzing booking data, calculating engagement metrics, identifying quality issues, or building data marts. You may solve a series of related queries that together address a larger business problem. The interviewer evaluates your SQL fluency, optimization instincts, data correctness reasoning, and ability to handle performance at scale. This round assesses both your technical capability and your ability to communicate SQL thinking clearly.
Tips & Advice
Practice writing SQL with relentless focus on performance and readability. Use window functions confidently (PARTITION BY, ORDER BY, ROWS/RANGE, LAG/LEAD, aggregates). Master complex joins including self-joins and multi-way joins. Be comfortable with CTEs and recursive CTEs. Understand indexing strategies and can read/interpret EXPLAIN plans. At Staff level, naturally optimize for both readability and performance. Discuss trade-offs between approaches. Test queries mentally against edge cases—NULLs, duplicates, late-arriving data, timezone issues. Think about scaling: will this query perform on billions of rows? Propose optimizations proactively. Demonstrate SQL patterns Airbnb would use in production.
Focus Topics
Data Quality and Edge Case Handling
Write queries correctly handling NULLs, duplicates, late-arriving data, and data inconsistencies. Add validation logic and assertions within queries. Identify data quality issues programmatically. Design idempotent queries safe for replay. Handle type conversions, timezone issues, and currency precision.
Practice Interview
Study Questions
Advanced Window Functions and Analytical Queries
Master window functions: ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, FIRST_VALUE, LAST_VALUE, SUM/AVG OVER windows. Understand frame specifications deeply (ROWS/RANGE, UNBOUNDED PRECEDING/FOLLOWING, CURRENT ROW). Build cumulative calculations, running averages, period-over-period comparisons using windows. Handle complex analytical scenarios like cohort analysis.
Practice Interview
Study Questions
Query Optimization and Execution Planning
Read and interpret EXPLAIN plans to identify bottlenecks. Understand index strategies (B-tree, hash, composite, covering indices). Optimize queries by reducing data scanned, choosing appropriate join strategies, and pushing predicates down. Rewrite queries for efficiency without changing logic. Understand how statistics influence optimization. Discuss cost-based vs. rule-based optimization.
Practice Interview
Study Questions
Complex Multi-Table Joins and Data Lineage
Handle multi-table joins with complex conditions, left/right/full outer joins, self-joins, cross joins. Understand join order optimization and performance implications. Build queries tracking data lineage and transformations across multiple sources. Debug join issues (e.g., many-to-many producing row explosion). Design queries that clearly communicate intent and data lineage.
Practice Interview
Study Questions
Data Modeling and ETL Architecture Round
What to Expect
A 60-minute technical round where you'll design data models and end-to-end ETL architectures for realistic Airbnb business problems. You might design a data warehouse schema for a new analytics use case, architect an ETL pipeline for streaming data ingestion, or optimize existing data architecture for new requirements. The interviewer provides business scenarios with constraints and ambiguities; you'll ask clarifying questions, propose solutions, discuss trade-offs, and defend your architectural choices. Evaluation focuses on your data modeling approach, understanding of ETL patterns, ability to balance trade-offs (consistency vs. availability, real-time vs. batch), systems thinking, and ability to make strategic architectural decisions.
Tips & Advice
Start with clarifying questions: What's the business problem? Data volume and growth trajectory? Query latency requirements? Data freshness needs? Who are consumers? Constraints? Propose an architecture, sketch it out, explain data flows. At Staff level, naturally consider scalability, fault tolerance, data quality, governance, and monitoring from the start. Discuss technology choices with justification. Explain trade-offs you've considered. Be ready to adapt your design based on feedback or changed requirements. Demonstrate both theoretical understanding and practical implementation experience. Show you can defend decisions with business/technical rationale. Use specific technologies Airbnb likely uses (Spark, Kafka, Presto, etc.).
Focus Topics
Scalability and Cost Optimization
Design systems scaling to Airbnb's petabyte scale. Consider partitioning, compression, materialization strategies. Discuss cost optimization—compute vs. storage trade-offs, tiering strategies, cloud cost management. Balance query performance with cost efficiency.
Practice Interview
Study Questions
Data Quality, Governance, and Monitoring Strategy
Propose data quality frameworks with validation rules, anomaly detection, and SLAs. Design data governance practices—data cataloging, lineage tracking, metadata management, access controls. Plan monitoring and alerting for pipeline health. Design data contracts between producers/consumers. Implement testing strategies (unit, integration, data validation tests).
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Study Questions
Data Warehouse vs. Data Lake vs. Lakehouse Architecture
Understand traditional data warehouses (structured, schema-on-write, strong consistency), data lakes (flexible, schema-on-read, cost-effective), and emerging lakehouse patterns. Discuss when to use each approach. Consider governance, query performance, cost, and use case requirements. Make strategic recommendations based on Airbnb's evolving data landscape.
Practice Interview
Study Questions
End-to-End ETL Pipeline Architecture
Design complete ETL pipelines: data extraction from source systems, transformation logic, data quality checks, loading into target systems. Choose batch vs. real-time processing based on requirements. Design for idempotency and fault tolerance. Select appropriate orchestration (Airflow, Spark jobs, etc.). Implement data quality frameworks and validation logic. Consider data lineage tracking and metadata management.
Practice Interview
Study Questions
Dimensional Data Modeling and Schema Design
Master dimensional modeling: fact tables (granularity selection, denormalization choices), dimension tables (slowly changing dimensions, Type 1/2/3, surrogate keys), conformed dimensions, bridge tables. Design schemas for Airbnb domains (bookings, reviews, hosts, listings, payments). Understand star schema vs. snowflake schema trade-offs. Normalize when appropriate; denormalize for analytics performance. Make deliberate modeling choices with clear rationale.
Practice Interview
Study Questions
Experience, Leadership and Impact Round
What to Expect
A 60-minute behavioral and experience-focused interview with a hiring manager or senior engineer that evaluates your professional trajectory, leadership maturity, and ability to drive organizational impact at Staff level. The interviewer will explore your most significant projects and initiatives in depth—understanding not just technical decisions but also your role in team dynamics, mentorship, cross-functional collaboration, and influence on technical strategy. Expect questions about how you've handled ambiguity, navigated difficult technical trade-offs, advocated for architectural changes, elevated team capabilities, or shaped data engineering practices. The conversation assesses alignment with Airbnb's values and your potential to grow within the organization.
Tips & Advice
Prepare detailed narratives about your career's most significant turning points and accomplishments. For each story, clearly articulate: the business challenge, your specific contributions (not 'we did'), measurable outcomes with metrics, and lessons learned. At Staff level, focus on stories demonstrating leadership, influence without authority, mentorship of senior colleagues, and strategic thinking. Be ready to discuss how you shaped technical decisions, advocated for architectural changes, or established best practices that influenced multiple teams. Share specific examples of engineers you mentored—their growth, their career outcomes, how you helped them. Discuss cross-functional collaboration: how you partnered with data scientists, analysts, product managers. Be authentic about failures and what you learned. Research Airbnb's culture and values, then authentically connect your experiences to those values. Ask thoughtful questions about data engineering challenges at Airbnb and team dynamics.
Focus Topics
Technical Decision-Making, Trade-offs, and Strategy
Share examples of significant technical decisions you've made and influenced. Explain the trade-offs considered (performance vs. complexity, real-time vs. batch, consistency vs. availability, cost vs. quality). Discuss how you made decisions collaboratively. Share how decisions evolved over time based on new constraints. Show that you can balance multiple factors and communicate rationale clearly to diverse audiences.
Practice Interview
Study Questions
Airbnb Values Alignment and Mission-Driven Work
Research Airbnb's stated values (belonging, innovation, creativity, etc.). Share authentic stories from your career demonstrating alignment with these values. Articulate why Airbnb's mission resonates with you personally. Discuss your vision for data engineering's role in Airbnb's mission. Show genuine passion for Airbnb's business challenges, not just the role.
Practice Interview
Study Questions
Mentorship and Development of Senior Colleagues
Share detailed examples of engineers you've mentored—their starting points, how you helped them grow, and their career outcomes. Discuss mentoring Senior-level or staff-track engineers, not just junior engineers. Explain your approach to accelerating learning and challenging high-performers. Demonstrate investment in others' success and ability to grow talent. Discuss how you balance hands-on work with developing others.
Practice Interview
Study Questions
Cross-Functional Collaboration and Stakeholder Leadership
Describe how you've worked effectively with data scientists, analysts, product managers, infrastructure teams, and leadership. Share examples of navigating conflicting priorities, building alignment across silos, and delivering value. Discuss how you've influenced teams outside your direct reporting structure. Show your approach to understanding and translating business requirements into technical solutions.
Practice Interview
Study Questions
Staff-Level Technical Leadership and Strategic Influence
Share specific examples of how you've driven significant technical decisions that shaped infrastructure or organizational practices. Discuss advocating for major architectural changes or proposing new technologies that benefited multiple teams. Demonstrate strategic thinking about data engineering challenges. Explain how your work amplified capabilities of engineers around you or unblocked multiple teams. Show ability to influence without authority.
Practice Interview
Study Questions
System Design: Large-Scale Data Platforms Round
What to Expect
A 60-minute technical round focused on designing large-scale, complex data platforms and systems. You might design a real-time analytics platform, a machine learning feature store's data infrastructure, a petabyte-scale data lake, a global event streaming platform, or a complex multi-tenant data integration system. The problem involves ambiguity and multiple valid approaches. You'll ask clarifying questions, propose a high-level architecture with detailed components, justify technology choices, dive deep into specific layers (ingestion, processing, storage, serving), and discuss operational concerns. The interviewer evaluates your ability to think at scale, make sophisticated architectural trade-offs, understand distributed systems constraints, and design for operational excellence.
Tips & Advice
Start with comprehensive clarifying questions: Latency requirements (batch vs. real-time)? Data volume and growth? Query patterns and frequency? Who are consumers? Failure tolerance and SLAs? Cost constraints? Regulatory/compliance requirements? Data consistency requirements? Sketch your architecture clearly, explaining each component and data flows. For Staff-level, go significantly deeper than surface-level architecture. Discuss specific technologies (Kafka, Flink, Spark, Presto, Druid, Delta Lake, etc.) and justify choices with trade-offs. Consider operational concerns: monitoring and alerting, debugging production issues, disaster recovery, auto-scaling, cost management. Think about edge cases and failure modes proactively. Be ready to adapt design based on interviewer feedback or changed constraints. Demonstrate that you've built similar systems—deep practical experience is expected at Staff level.
Focus Topics
Reliability, Observability, and Operational Excellence
Design comprehensive monitoring and alerting for data pipeline health. Implement data quality checks, data contracts, and SLAs. Design disaster recovery, backup, and restore strategies. Plan for observability: logging, metrics, tracing, dashboards. Design runbooks and escalation procedures. Consider cross-region replication and failover for high availability.
Practice Interview
Study Questions
Cost Optimization and Resource Efficiency at Scale
Design for cost-effectiveness at Airbnb's scale. Make informed trade-offs between compute cost, storage cost, and latency. Discuss tiering strategies (hot/warm/cold data), compression techniques, query optimization for cost reduction. Understand cloud pricing models (AWS on-demand vs. reserved vs. spot). Design for efficient resource utilization.
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Study Questions
Distributed Storage Architecture and Format Optimization
Design distributed storage architectures balancing data warehouses, data lakes, and lakehouse patterns. Choose appropriate storage formats (Parquet, ORC, Delta, Iceberg) and understand compression trade-offs. Design partitioning and bucketing strategies for query performance. Understand object storage (S3) vs. distributed filesystems (HDFS). Plan metadata management, schema registry, and version control.
Practice Interview
Study Questions
Query Serving Layer and Multi-Use-Case Analytics
Design query engines serving diverse use cases: SQL analytics (Presto/Trino), real-time serving (NoSQL, search), machine learning (feature stores). Optimize for latency, throughput, and cost. Design caching strategies (in-memory stores like Redis). Plan for multi-tenancy and resource isolation. Discuss indices, materialized views, and query optimization at scale.
Practice Interview
Study Questions
Data Ingestion at Scale: Batch and Real-Time
Design systems ingesting data at petabyte scale from diverse sources (APIs, databases, logs, events, IoT). Choose between batch and streaming ingestion based on requirements. Design for fault tolerance, exactly-once semantics, and late-arriving data handling. Understand technologies: Kafka for streaming, Kinesis for AWS, batch jobs for scheduled ingestion. Plan schema evolution and format compatibility. Design for data quality and validation at ingestion.
Practice Interview
Study Questions
Frequently Asked Data Engineer Interview Questions
Sample Answer
Sample Answer
CREATE TABLE metrics_raw (
org_id UInt32,
metric_name LowCardinality(String),
tags Nested(key String, value String),
ts DateTime64(3),
value Float64
) ENGINE = MergeTree()
PARTITION BY toDate(ts)
ORDER BY (org_id, metric_name, ts)
TTL ts + INTERVAL 7 DAY DELETE
SETTINGS index_granularity = 8192;CREATE TABLE metrics_min (
org_id UInt32,
metric_name LowCardinality(String),
minute DateTime,
max_value Float64,
min_value Float64,
sum_value Float64,
count UInt64
) ENGINE = SummingMergeTree()
PARTITION BY toYYYYMM(minute)
ORDER BY (org_id, metric_name, minute)
TTL minute + INTERVAL 365 DAY;SELECT minute, max_value
FROM metrics_min
WHERE org_id = 42 AND metric_name = 'cpu.user'
AND minute BETWEEN '2025-11-20 00:00:00' AND '2025-11-20 01:00:00'
ORDER BY minute;SELECT toStartOfHour(minute) as hour,
max(max_value) AS hour_max,
sum(sum_value) AS hour_sum,
sum(count) AS hour_count
FROM metrics_min
WHERE org_id=42 AND metric_name='cpu.user' AND minute >= now() - INTERVAL 7 DAY
GROUP BY hour;SELECT toStartOfHour(ts) as hour, max(value) as hour_max
FROM metrics_raw
WHERE org_id=42 AND metric_name='cpu.user' AND ts BETWEEN ...
GROUP BY hour;Sample Answer
Sample Answer
Sample Answer
import heapq
from typing import Iterable, List
def k_smallest_stream(stream: Iterable[int], k: int) -> List[int]:
"""
Return k smallest elements from a stream using O(k) extra memory.
If k == 0 returns [].
Result is unsorted; sort if needed.
"""
if k <= 0:
return []
max_heap = [] # store negatives to emulate max-heap
for x in stream:
if len(max_heap) < k:
heapq.heappush(max_heap, -x)
else:
if x < -max_heap[0]:
heapq.heapreplace(max_heap, -x)
# convert back to positive values
result = [-v for v in max_heap]
result.sort() # optional: return sorted ascending
return resultSample Answer
Sample Answer
WITH raw_events AS (
-- source event stream: user_id, event_type, event_ts (timestamp)
SELECT user_id, event_type, event_ts::timestamptz FROM analytics.events
WHERE event_type IN ('signup','onboarding_completed') OR event_type = 'conversion'
),
-- 1) Activation: find first signup and the first onboarding after signup (can be in different sessions)
first_signup AS (
SELECT user_id, MIN(event_ts) AS signup_ts
FROM raw_events
WHERE event_type = 'signup'
GROUP BY user_id
),
first_onboard_after_signup AS (
SELECT s.user_id, MIN(e.event_ts) AS onboarding_ts
FROM first_signup s
JOIN raw_events e
ON s.user_id = e.user_id
AND e.event_type = 'onboarding_completed'
AND e.event_ts >= s.signup_ts
GROUP BY s.user_id
),
activation AS (
-- activation_date is the date of onboarding completion (could choose signup date instead)
SELECT s.user_id,
DATE_TRUNC('day', o.onboarding_ts) AS activation_date,
s.signup_ts, o.onboarding_ts
FROM first_signup s
JOIN first_onboard_after_signup o USING (user_id)
),
-- 2) Event aggregation: capture conversion events per user per day relative to activation
user_conversions AS (
SELECT a.user_id,
a.activation_date,
DATE_TRUNC('day', e.event_ts) AS event_date,
MIN(e.event_ts) AS first_conversion_ts -- if multiple conversions same day
FROM activation a
JOIN raw_events e
ON a.user_id = e.user_id
AND e.event_type = 'conversion'
AND e.event_ts >= a.onboarding_ts -- only post-activation conversions
GROUP BY 1,2,3
),
-- 3) Cohort metrics: compute whether user converted within N days
cohort_user_windows AS (
SELECT a.user_id,
a.activation_date,
(EXTRACT(epoch FROM (uc.first_conversion_ts - a.onboarding_ts))/86400)::int AS days_to_conversion
FROM activation a
LEFT JOIN (
SELECT user_id, MIN(first_conversion_ts) AS first_conversion_ts
FROM user_conversions
GROUP BY user_id
) uc USING (user_id)
),
final AS (
SELECT
activation_date,
DATE_PART('week', activation_date)::int AS cohort_week,
COUNT(*) AS cohort_size,
COUNT(CASE WHEN days_to_conversion = 0 THEN 1 END) AS conv_day_0,
COUNT(CASE WHEN days_to_conversion BETWEEN 0 AND 1 THEN 1 END) AS conv_1d,
COUNT(CASE WHEN days_to_conversion BETWEEN 0 AND 6 THEN 1 END) AS conv_7d,
COUNT(CASE WHEN days_to_conversion BETWEEN 0 AND 29 THEN 1 END) AS conv_30d
FROM cohort_user_windows
GROUP BY 1
ORDER BY activation_date
)
SELECT *, conv_1d::float / cohort_size AS conv_1d_rate,
conv_7d::float / cohort_size AS conv_7d_rate,
conv_30d::float / cohort_size AS conv_30d_rate
FROM final;Sample Answer
CREATE TABLE transactions(product_id INT, product_name TEXT, amount INT);
INSERT INTO transactions VALUES
(1,'A', 10),
(2,'B', 10),
(1,'A', 5),
(2,'B', 5);
-- Both product 1 and 2 have SUM(amount)=15SELECT product_id, product_name, SUM(amount) AS total
FROM transactions
GROUP BY product_id, product_name
ORDER BY total DESC;-- deterministic ordering by total desc, then product_id asc
SELECT product_id, product_name, total
FROM (
SELECT product_id, product_name, SUM(amount) AS total
FROM transactions
GROUP BY product_id, product_name
) t
ORDER BY total DESC, product_id ASC;
-- or produce deterministic rank
SELECT product_id, product_name, total,
ROW_NUMBER() OVER (ORDER BY total DESC, product_id ASC) AS rank
FROM (
SELECT product_id, product_name, SUM(amount) AS total
FROM transactions
GROUP BY product_id, product_name
) t;Sample Answer
Sample Answer
Recommended Additional Resources
- Designing Data-Intensive Applications by Martin Kleppmann
- Fundamentals of Data Engineering by Joe Reis and Matt Housley
- The Art of PostgreSQL by Dimitri Fontaine
- Spark: The Definitive Guide by Bill Chambers and Matei Zaharia
- System Design Interview by Alex Xu
- Building Secure and Reliable Systems by Heather Adkins, Betsy Beyer, Paul Blankinship, Ana Oprea, Piotr Lewandowski, Adam Stubblefield
- LeetCode Premium (focus on medium-hard data structure and algorithm problems)
- HackerRank data engineering track
- Mode Analytics SQL Tutorial
- Airbnb Engineering Blog (airbnb.io) for technical insights
- Blind community discussions on Airbnb interview experiences
- Exponent Data Engineer Interview Prep
- DataCamp Apache Spark and SQL courses
- Levels.fyi Airbnb compensation and interview data
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