Data Engineer Interview Guide 2026

The Data Engineer Interview Loop

Data engineer loops vary more by company than SWE loops, but the standard at FAANG and top unicorns is: 1-2 SQL rounds, 1 coding round (Python/Scala, algorithm problems), 1 data pipeline design round, and 1 behavioral round. Some companies add a data modeling round or a take-home case study.

The trap DE candidates fall into: over-indexing on SQL and neglecting the coding and system design rounds. A strong SQL candidate who cannot write clean Python or design a scalable Kafka pipeline will fail mid-level DE interviews at FAANG.

SQL: The Foundation

SQL for DE interviews goes beyond basic SELECTs. The tested topics: window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, running totals), CTEs vs subqueries vs temp tables, joins (including SELF JOIN and CROSS JOIN use cases), aggregations with HAVING, and query optimization (index awareness, explain plans).

Most common DE SQL questions from LeakCode's database: find the second-highest salary, calculate 7-day rolling averages, identify users with consecutive activity, compute retention cohorts, and sessionize clickstream data. Practice all of these until they are reflexive.

Performance questions are common at senior levels: "This query on a 10TB table is slow, how do you diagnose and fix it?" Know partitioning strategies, columnar storage (Parquet vs ORC), and when to use materialized views.

Pipeline Design and Data Architecture

Pipeline design rounds ask you to architect data systems: design a real-time analytics pipeline for a ride-sharing app, design a data warehouse for an e-commerce company, design an event-driven ETL pipeline. The framework: sources and ingestion, transformation layer, storage and serving layer, orchestration and scheduling, monitoring.

Key concepts to know: Lambda vs Kappa architecture, batch vs streaming tradeoffs (Spark vs Flink vs Kafka Streams), data lake vs data warehouse vs lakehouse, slowly changing dimensions (SCD Type 1/2/3), idempotency in pipelines, and exactly-once semantics.

Coding for Data Engineers

Coding rounds test Python or Scala at medium LeetCode difficulty, plus data-specific problems: parsing semi-structured JSON logs, implementing a simple map-reduce, writing unit tests for a pipeline function. Know Pandas and PySpark APIs well enough to write them without documentation.

Data engineering coding interviews often involve data cleaning and transformation logic: handle nulls, parse dates from inconsistent formats, deduplicate records, join two datasets with mismatched keys. These are more practical than abstract algorithm problems but require the same attention to edge cases.