Data Engineer Interview Prep Guide
Master data engineering interviews with ETL pipeline design, data modeling, SQL optimization, Spark, and distributed computing questions asked at Databricks, Snowflake, Amazon, and Google.
Last Updated: 2026-02-11 | Reading Time: 10-12 minutes
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Interview Types
Key Skills to Demonstrate
Top Data Engineer Interview Questions
Design a data pipeline for processing 10 million events per day from a mobile app into an analytics warehouse. (Netflix/Uber-style)
Use the 3-hop architecture (bronze/silver/gold layers). Cover: ingestion (Kafka or Kinesis for real-time, S3 landing zone for batch), bronze layer (raw storage in Parquet/Delta), silver layer (cleaned and deduplicated with Spark/dbt), gold layer (aggregated tables for analytics). Discuss orchestration (Airflow DAGs with idempotent tasks), monitoring (data freshness, row count anomalies, schema drift), and cost optimization (partitioning by date, file compaction to 128-256MB files for optimal Spark parallelism).
Write a SQL query to implement an incremental update: find new and changed records since the last pipeline run. (Asked at Meta, Amazon)
Use a merge/upsert pattern: compare source and target on business keys. For new records, INSERT. For changed records, UPDATE. Use watermark columns (updated_at) for incremental extraction. Discuss handling late-arriving data, idempotency (what happens if the pipeline runs twice), and the difference between SCD Type 1 (overwrite) and SCD Type 2 (history tracking). Mention that in PySpark this uses the MERGE INTO pattern on Delta tables.
Your Spark job is failing with an OOM error on a 500GB dataset. How do you debug and fix it?
Systematic approach: (1) Check executor logs to identify the failing stage, (2) Look for data skew (one partition much larger than others) using Spark UI, (3) Check for exploding joins causing cartesian products, (4) Solutions: increase spark.sql.shuffle.partitions, use salting to handle skewed keys, enable AQE (Adaptive Query Execution), broadcast small tables, repartition before expensive operations, tune executor memory and overhead. Mention optimal file sizes (128-256MB per partition) and avoiding too many small files.
Explain the difference between star schema and snowflake schema. When would you choose each? (Common at all companies)
Star schema denormalizes dimensions into single flat tables for fast query performance and simplicity. Snowflake schema normalizes dimensions into sub-tables for storage efficiency and data integrity. Choose star schema for analytics/BI workloads where query speed matters (most modern data warehouses). Choose snowflake for systems with frequent dimension updates or strict storage constraints. Discuss the practical reality: most modern cloud warehouses (Snowflake, BigQuery, Databricks) handle star schemas efficiently and storage is cheap, making star schemas the default choice for analytics.
How do you ensure data quality in a production pipeline? Walk me through your approach. (Asked at Airbnb, Stripe)
Layer your approach: (1) Schema validation at ingestion (reject malformed records to dead letter queue), (2) Row-level checks in transformation (null checks, range validation, referential integrity), (3) Dataset-level checks post-transformation (row count anomaly detection, distribution drift, freshness SLAs), (4) Tooling: Great Expectations or dbt tests for automated validation, Monte Carlo or Soda for data observability. Discuss alerting strategy: not every anomaly is critical, define severity tiers and escalation paths. Mention data contracts between producer and consumer teams.
Explain the difference between batch and stream processing. When would you choose each?
Batch processes bounded datasets on a schedule (Spark, dbt), streaming processes unbounded data continuously (Flink, Kafka Streams, Spark Structured Streaming). Choose based on latency requirements: batch for analytics where hourly/daily freshness is fine, streaming for real-time dashboards, fraud detection, or operational systems. Discuss Lambda architecture (batch + streaming in parallel) vs Kappa architecture (streaming-only with replay). Mention that many modern architectures use micro-batch (Spark Structured Streaming) as a middle ground.
Tell me about a data pipeline that failed in production. How did you handle it?
Structure with STAR but emphasize the debugging methodology: how you detected the failure (monitoring/alerting vs user report), how you diagnosed the root cause (logs, data profiling, pipeline lineage), the fix, and most importantly what guardrails you added to prevent recurrence (data quality checks, circuit breakers, dead letter queues, runbook documentation). Show you think about pipeline reliability as a system, not just fixing individual bugs.
Design a data model for a ride-sharing platform that supports both operational reporting and ML feature engineering. (Uber/Lyft-style)
Start with operational entities: riders, drivers, trips, payments, ratings. Design a star schema with trip_fact at the center and rider_dim, driver_dim, location_dim, time_dim as dimensions. For ML features, design a feature store layer: driver reliability score (rolling average of ratings, cancellation rate), rider LTV (cumulative spend, trip frequency), surge pricing features (demand/supply ratio by location and time). Discuss slowly changing dimensions, data freshness requirements for ML vs reporting, and partitioning strategy.
How to Prepare for Data Engineer Interviews
Master Advanced SQL Under Time Pressure
SQL is the most tested skill in every data engineering interview. Practice complex queries daily on platforms like LeetCode, StrataScratch, or DataLemur. Focus on window functions (ROW_NUMBER, RANK, LAG/LEAD, running totals), CTEs for readability, recursive queries, self-joins for gap analysis, and query plan analysis. At Meta and Google, questions are product-oriented, so restate the business problem before writing SQL. Target completing each problem in 10-15 minutes.
Learn Spark Internals and Optimization
Understand the Spark execution model: driver vs executors, lazy evaluation, narrow vs wide transformations, shuffle operations, and how data is partitioned. Know how to diagnose and fix common issues: data skew (salting), OOM errors (repartitioning, broadcast joins), small file problems (coalesce, compaction), and slow shuffles (tuning spark.sql.shuffle.partitions). Practice writing PySpark jobs that would work at scale, not just on sample data.
Study Data Modeling for Both Analytics and ML
Know dimensional modeling (Kimball star/snowflake schemas), Data Vault 2.0, and One Big Table patterns. Understand when each is appropriate. For modern stacks, know how dbt implements modeling patterns (staging, intermediate, mart layers). Be ready to design schemas that support both analytics queries and ML feature engineering pipelines. Practice modeling real business domains: e-commerce, ride-sharing, social media, financial transactions.
Build an End-to-End Pipeline Project
Create a project that demonstrates the full modern data stack: ingestion from an API or streaming source, landing in a data lake (S3/GCS), transformation with dbt or Spark, loading into a warehouse (Snowflake or BigQuery), orchestration with Airflow, data quality checks with Great Expectations, and a simple dashboard. This single project is more valuable than any certification and gives you concrete examples to discuss in interviews.
Know the Modern Data Stack Ecosystem
Stay current with the tools companies actually use in 2026: dbt for transformation, Snowflake/Databricks/BigQuery for warehousing, Airflow/Dagster for orchestration, Kafka/Confluent for streaming, Fivetran/Airbyte for ingestion, Great Expectations/Monte Carlo for data quality. Know the tradeoffs between competing tools (e.g., Snowflake vs Databricks, Airflow vs Dagster) even if you only use one at your current job.
Data Engineer Interview Formats
SQL Assessment
Complex SQL queries testing advanced joins, aggregations, window functions, CTEs, and query optimization. At Meta and Amazon, questions are product-oriented: you are given a business scenario and must write SQL to answer analytical questions. Expect 2-3 problems in 45 minutes. Performance matters: discuss why your query is efficient, how indexes help, and what the query plan looks like. Some companies use live coding on shared editors.
Pipeline / System Design
Design an end-to-end data pipeline for a given scenario (e.g., real-time analytics for a marketplace, ML feature store, event-driven data lake). Interviewers evaluate your ability to choose appropriate tools, design for scale and reliability, handle failure scenarios, and optimize for cost. Use the 3-hop architecture as a starting framework. Discuss orchestration, monitoring, data quality, and SLAs. At Google and Netflix, expect follow-up questions about specific failure modes.
Python Coding Round
Data transformation and manipulation tasks in Python, often involving parsing, cleaning, aggregating, or pipeline logic. May include PySpark code for distributed processing. Focus on writing clean, testable code with error handling. Some companies include algorithmic problems at LeetCode easy/medium difficulty. A few companies (Databricks, Snowflake) include take-home assignments with real datasets.
Common Mistakes to Avoid
Writing SQL or code immediately without clarifying the problem
At companies like Meta and Google, data engineering questions are product-oriented and test reasoning, not just syntax. Before writing any code, restate the problem in your own words, ask about data volume and latency requirements, clarify edge cases (nulls, duplicates, late-arriving data), and outline your approach. This takes 2-3 minutes but dramatically improves your answer quality.
Ignoring data quality in pipeline design answers
Every pipeline answer should include data validation at multiple layers, error handling with dead letter queues, monitoring with SLA alerting, and idempotent operations for safe re-runs. Data quality is consistently rated as the top priority for data engineering teams. Not mentioning it signals you have not worked on production pipelines.
Not understanding scale and cost implications
Always ask about data volume, velocity, and growth rate. Design differently for 1GB/day vs 1TB/day. Discuss partitioning strategy, file format choices (Parquet vs JSON), compression, and cloud cost optimization (spot instances, auto-scaling, storage tiering). At companies like Netflix and Uber, cost efficiency is a key evaluation criterion.
Weak understanding of distributed computing fundamentals
Know how Spark distributes work across executors, what causes expensive shuffles, how to optimize partitioning for parallel processing, and why small files are a problem (aim for 128-256MB per file). Understand CAP theorem implications for streaming systems. Practice explaining distributed concepts with concrete examples, not just theory.
Data Engineer Interview FAQs
SQL or Python: which is more important for data engineering interviews?
Both are essential and tested separately. SQL is the most universally tested skill across every data engineering interview and you cannot skip it. Python is needed for pipeline code, PySpark, Airflow DAGs, and automation scripts. If you must prioritize, get to advanced SQL first (window functions, CTEs, query optimization), then ensure strong Python data manipulation skills (Pandas, file handling, API integration).
Do I need to know Spark for data engineering interviews in 2026?
For mid to senior roles at most tech companies, yes. Spark (especially PySpark) remains the dominant distributed processing framework. Understand its architecture (driver/executor model), DataFrame API, partitioning and shuffle optimization, and common failure modes (OOM, data skew). Even if your current job uses dbt on Snowflake, Spark knowledge is expected for system design discussions about scale.
What is the difference between data engineer and data scientist?
Data engineers build the infrastructure, pipelines, and platforms that data scientists use. Engineers focus on reliability, scalability, data quality, and SLAs. Scientists focus on analysis, modeling, and generating business insights. The overlap is growing: modern data engineers need to understand ML feature engineering, and data scientists increasingly need SQL and pipeline skills. Compensation is comparable, with data engineers averaging $130,000-$186,000 in 2026.
How important is dbt knowledge for data engineering interviews in 2026?
Increasingly important. dbt has become the standard transformation tool in the modern data stack, used by companies from startups to enterprises. Know how dbt models work (staging, intermediate, mart layers), how tests and documentation are defined, incremental models, and how dbt fits into the broader pipeline alongside orchestration tools like Airflow. Even if your current job uses Spark for everything, understanding dbt signals you know the modern data stack.
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Last updated: 2026-02-11 | Written by JobJourney Career Experts