Data Engineer Interview Prep — All Articles
SQL, Python, Spark, Airflow, dbt, AWS, BigQuery — everything for data engineering interviews from associate to senior level.
Data engineering roles require SQL, Python, pipeline design, and cloud knowledge. These articles cover all of it.
SQL for Data Engineers
Advanced SQL for pipeline validation, deduplication, incremental loads, and schema design.
- Incremental load patterns
- MERGE / UPSERT statements
- Data quality checks in SQL
Python for Pipelines
Python scripting for ETL — file handling, API calls, data transformation, and scheduling.
- requests + boto3 for data ingestion
- Pandas for transformation
- pathlib and os for file ops
ETL & ELT Concepts
ETL vs ELT, batch vs streaming, incremental loads, CDC — the conceptual knowledge tested in DE interviews.
dbt
Models, refs, tests, materialisation — dbt is now expected in most modern DE job descriptions.
- Incremental models
- dbt tests (not_null, unique)
- Lineage and documentation
Airflow
DAGs, operators, sensors, and scheduling — conceptual and practical questions from DE interviews.
- DAG structure and dependencies
- Backfill and catchup
- Monitoring and alerting
Cloud Platforms
AWS (S3, Redshift, Glue), GCP (BigQuery, Dataflow), Azure (Synapse) — cloud is mandatory for senior DE roles.
- S3 + Redshift architecture
- BigQuery partitioning & clustering
- Snowflake compute separation
Our most comprehensive data engineering resource — covers all rounds of the DE interview process.
⚙️ Data Engineer Interview Guide 2026
SQL, Python, Spark, Airflow, dbt, cloud platforms, and system design for data pipelines. Covers associate to senior level interviews at top companies.
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