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.

Spark and Distributed Compute Rounds

PySpark or Scala Spark fluency is expected at most data engineering interviews. Interviewers probe whether you understand the execution model: Spark builds a DAG of transformations (lazy), triggers execution at actions, shuffles data between stages for wide transformations (groupBy, join, distinct). Each shuffle is expensive; one of the discriminator skills is minimizing shuffles in your job.

Common probes: when do you broadcast a smaller dataset in a join (when one side fits in executor memory, typically less than 10MB-1GB depending on cluster config). What is the difference between cache and persist (cache is persist with MEMORY_ONLY default). What does coalesce vs repartition do (coalesce reduces partitions without shuffle, repartition fully shuffles to achieve target partition count). Reports on LeakCode show these probes appearing at Amazon Data Engineer, Stripe Data Eng, and Netflix Data Eng interviews.

Pipeline Design Round

Data engineering's analog to the system design round is the pipeline design round. Typical prompts: design a daily ETL that ingests user events from Kafka, deduplicates, and loads into a data warehouse. Design a near-real-time feature store for ML serving. Design a data quality monitoring system that flags anomalies in downstream metrics.

The grading rubric weighs: choice of batch vs streaming (and articulating the latency/cost trade-off), idempotency design (so reruns produce the same output), schema evolution strategy (additive vs breaking changes, backward and forward compatibility), failure handling (retries, dead letter queues, alerting), data quality checks (row counts, null rates, schema drift detection). Strong candidates surface these explicitly without prompting.

SQL Depth for Data Engineers

Data engineering SQL rounds probe deeper than software engineering SQL rounds. Expect complex window functions, recursive CTEs, query optimization with EXPLAIN ANALYZE, partitioning and clustering strategies, slowly-changing dimensions (SCD Type 1, 2, 3, 6), star vs snowflake schema design.

Modern data warehouse specifics that interviewers probe: BigQuery (clustered tables, partition expiration, slot management), Snowflake (virtual warehouses, micro-partitions, time travel), Databricks/Delta Lake (ACID on object storage, change data feed). At least one of these stacks should be in your fluency depth depending on the target company.

Tooling and Orchestration

Be fluent in at least one orchestrator: Airflow (most common, DAG-based), Dagster (asset-centric, modern alternative), Prefect (Python-native), dbt (transformation layer, not orchestration but often paired). Interview probes: how do you handle a task that needs to run after multiple upstream tasks complete? What is the difference between an XCom in Airflow and using an external state store? How do you test a DAG locally before deploying?

Streaming systems: Kafka or its variants (Kinesis, Pulsar). Understand offsets, consumer groups, partitioning, retention. Flink and Spark Streaming for stateful stream processing. The discriminator at senior data engineering interviews is whether you can articulate when batch is appropriate vs streaming, and the operational cost difference.