Data engineering interview sprint
Pipelines, warehouses, Spark, dbt, and data quality — interview-ready in 30 days.
Universal data engineering interview prep: SQL, modeling, batch/stream pipelines, quality, and cloud warehouses. Pair with the Data fundamentals elective if you need a structured SQL/Python refresh, and a regional elective for visa sponsorship guidance.
Who this is for: Engineers targeting data platform, analytics, or DE roles globally.
outcomes
- Design batch and streaming pipelines with clear SLAs
- Answer SQL, modeling, and data quality interview questions
- Discuss Spark, Airflow/dbt, and warehouse trade-offs confidently
related roadmaps
Regional electives, shared foundations, and sibling sprints — pick what matches your target role.
lessons
- 1 Week 0: Your 30-day battle plan Universal schedule, pass gates, and how to pick a regional elective.
- 2 Week 1: Foundations interview bar Baseline every loop tests before depth topics.
- 3 Week 1 checkpoint Self-audit before Week 2 depth.
- 4 Week 2: Data modeling & warehousing Star schema, grain, medallion.
- 5 Week 2: Batch pipelines & orchestration Airflow, dbt, backfills.
- 6 Week 3: Streaming & CDC Kafka, late data.
- 7 Week 3: Data quality & observability Tests, SLAs, incidents.
- 8 Week 3: Cloud warehouses & lakehouse Snowflake, BigQuery, Databricks.
- 9 Week 4: Behavioral prep STAR stories that work in any culture — tune examples in your elective.
- 10 Week 4: Mock drills & interview day Timed technical + portfolio + behavioral practice.
- 11 Complete pass checklist Final audit before booking interviews.