💻Technical Questions
Q1Design a data pipeline that ingests clickstream data from a web application (500K events/hour) into a data warehouse.
💡Event collection (SDK/API), message queue (Kafka), stream processing or micro-batch, data lake (S3), transformation (Spark/dbt), loading to warehouse (Snowflake/BigQuery), and monitoring.
Q2What is the difference between a star schema and a snowflake schema? When would you use each?
💡Star: denormalized, faster queries, more storage. Snowflake: normalized dimensions, less storage, more joins. Star schema is preferred for analytics/BI workloads. Snowflake for slowly changing dimensions.
Q3Write a SQL query to find the top 3 products by revenue for each category in the last 30 days.
💡Window function with ROW_NUMBER() or RANK() partitioned by category, ordered by revenue descending. Filter for last 30 days in WHERE clause.
Q4How do you handle schema evolution in a data pipeline?
💡Forward/backward compatibility, schema registry, versioning, handling of new/removed/renamed columns, and migration strategies for downstream consumers.
Q5Explain the difference between batch processing and stream processing. When would you choose each?
💡Batch: high throughput, higher latency (Spark, Airflow). Stream: low latency, lower throughput (Kafka, Flink). Use cases: batch for analytics, stream for real-time features, monitoring, fraud detection.
Q6How do you ensure data quality in a production pipeline?
💡Input validation, schema validation, Great Expectations or dbt tests, data profiling, anomaly detection, SLA monitoring, and data lineage tracking.
Q7Design a data lake architecture that supports both analytics and ML workloads.
💡Landing zone → raw → curated layers. Partitioning strategy, file formats (Parquet), catalog (Glue/Hive), access patterns for analytics vs. ML, and governance.
🧠Behavioral Questions
B1Tell me about a data pipeline failure that impacted downstream users. How did you handle it?
💡Show incident response: detection, communication, debugging, fix, and prevention. Mention monitoring improvements you implemented afterward.
B2Describe a data platform decision you made that had significant long-term impact.
💡Show the decision context, alternatives considered, trade-offs evaluated, and how it played out. Mention what you'd do differently in hindsight.
🎯Situational Questions
S1Your nightly Spark job that took 2 hours now takes 8 hours after data volume doubled. How do you optimize it?
💡Data skew analysis, partition optimization, broadcast joins for small tables, caching, predicate pushdown, right-sizing executors, and evaluating incremental processing.
S2A data analyst reports that numbers in their dashboard don't match the source system. How do you investigate?
💡Compare row counts, check for duplicates, verify join logic, check for late-arriving data, timezone issues, null handling, and incremental load boundaries.
Must-Know Topics
- ✓SQL (advanced — window functions, CTEs, optimization)
- ✓Data modeling (star schema, slowly changing dimensions)
- ✓Apache Spark / PySpark
- ✓Orchestration (Airflow, Dagster)
- ✓Data warehousing (Snowflake, BigQuery)
- ✓Streaming (Kafka, Flink)
- ✓dbt for transformations
- ✓Data quality and testing
Common Interview Mistakes to Avoid
- ✗Not knowing advanced SQL — window functions, CTEs, and query optimization are tested in every interview
- ✗Designing pipelines without considering data quality, monitoring, and failure handling
- ✗Not understanding trade-offs between batch and stream processing
- ✗Ignoring cost optimization — data engineering interviews increasingly test cost awareness
- ✗Not having experience with modern data stack tools (dbt, Airflow) that employers expect
Frequently Asked Questions
What do data engineer interviews test?▼
Four areas: (1) SQL — advanced queries with window functions and optimization, (2) Data modeling — schema design for analytics, (3) System design — pipeline architecture for given requirements, (4) Coding — Python for data processing and pipeline logic.
How much SQL do I need to know for data engineer interviews?▼
Advanced SQL is essential — window functions (RANK, LAG, LEAD, SUM OVER), CTEs, self-joins, query optimization (EXPLAIN), and complex aggregations. Most interviews include 1–2 SQL problems. Practice on LeetCode SQL and StrataScratch.
Are system design questions common in data engineer interviews?▼
Yes, at mid-senior levels. You'll be asked to design data pipelines, data lakes, or real-time processing systems. Focus on: data flow, tool selection with trade-offs, schema design, fault tolerance, and monitoring.
Should I prepare LeetCode-style coding for data engineer interviews?▼
Yes, but focus on data-related problems: string parsing, data transformation, array manipulation, and basic algorithms. Python is the preferred language. Most companies test medium-difficulty coding problems.
What's the best way to prepare for data engineer interviews?▼
4-week plan: Week 1: SQL (50+ problems on window functions, CTEs). Week 2: Data modeling (star schema, dimension types). Week 3: System design (pipeline architecture, tool trade-offs). Week 4: Coding (Python data processing problems) + mock interviews.
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