Data Engineer Interview Preparation Hub

Everything you need to prep for data engineer interviews in 2026 - a 14-day structured study plan, 16+ topic-focused cheat sheets, mock questions, and deep-dive articles. Designed for mid-to-senior roles at data-heavy companies (FAANG, fintech, high-scale SaaS).

The 14-day study plan

Work through one focus area per day. Each day combines a cheat sheet review (30-45 min) with 5-10 mock questions (30 min) and one deep-dive article (30 min). Total: ~2 hours/day for 2 weeks.

Week 1: Core fundamentals

Day 1 - SQL fundamentals: joins, group by, window functions, CTEs. Cheat sheet: SQL Window Functions.
Day 2 - SQL interview patterns: top-N per group, gaps-and-islands, pivots. Cheat sheet: SQL Interview Questions.
Day 3 - Python for data: pandas, list comprehensions, generators. Cheat sheet: Python for DE.
Day 4 - Data modeling: star vs snowflake schema, SCD types, normalization. Cheat sheet: Data modeling.
Day 5 - Warehousing: OLTP vs OLAP, columnar storage, partitioning. Cheat sheet: Snowflake SQL.
Day 6 - Orchestration: Airflow DAGs, dependencies, retries, SLAs. Cheat sheet: Airflow Essentials.
Day 7 - Transformation: dbt models, tests, macros, incremental. Cheat sheet: dbt Commands.

Week 2: Platform + system design

Day 8 - Snowflake deep dive: virtual warehouses, micro-partitions, clustering. Cheat sheet: Snowflake Query Tuning (Interview).
Day 9 - PySpark: lazy eval, shuffles, broadcast joins, skew. Cheat sheet: PySpark cheat sheet.
Day 10 - Streaming: Kafka partitions, exactly-once, watermarks. Cheat sheet: Snowpipe Streaming & Kafka (Interview).
Day 11 - Cloud storage: S3 formats, Parquet vs Iceberg, lifecycle policies. Cheat sheet: AWS for Data Engineers.
Day 12 - System design: batch vs streaming, CAP theorem, idempotency. Cheat sheet: Data Engineering Interview Questions.
Day 13 - Behavioral + STAR: conflict, ownership, on-call. Cheat sheet: Snowflake Interview Questions.
Day 14 - Mock + review: one end-to-end mock interview, review weak areas.

Cheat sheets by topic

Each cheat sheet is tightly scoped, runnable, and includes many code snippets. Filter by SQL, orchestration, cloud platforms, programming, architecture, interview-specific, or best practices.

Interview format at top companies

FAANG (Amazon, Meta, Google): 2 SQL rounds (advanced window functions, optimization), 1 system design (lambda architecture), 1-2 coding (Python/Spark), 1 behavioral (STAR).
Fintech (Stripe, Robinhood): heavy on correctness/consistency (idempotency, exactly-once), strong SQL, moderate coding.
SaaS scale-ups: dbt/Snowflake/Airflow deep dives, cost-aware design, more ownership questions.

Common question categories

SQL: top-N per group, cumulative totals, gap detection, pivoting, slowest queries
Data modeling: design schema for X, SCD Type 2 implementation, fact vs dim
Pipelines: design daily sales dashboard, handle late-arriving data, backfill strategy
System design: real-time recommendations, data lake + warehouse, streaming ETL
Behavioral: hardest incident, disagreement with PM, on-call horror story