Data Engineer Roadmap
DE interviews test whether you can build reliable data products: correct, cost-efficient, and understandable.
Core competencies
- SQL under time
- joins, group by, window functions
- correctness and performance intuition
- Data modeling
- dimensional vs normalized models
- keys, grain, slowly changing dimensions
- Pipelines and orchestration
- batch vs streaming
- idempotency, backfills, reprocessing
- Data quality and reliability
- freshness, completeness, accuracy
- lineage and governance
- cost control
10-week plan
Weeks 1–2: SQL drill
- 4 timed SQL sessions/week
- keep a mistake log by failure type
Weeks 3–4: Modeling
- design schemas for common domains (orders, payments, events)
Weeks 5–7: Pipelines
- design batch + streaming pipelines
- handle late data and schema evolution
Weeks 8–10: Mock DE loop
- SQL + system design (data platform) + behavioral
Common failure cases
- wrong assumptions about data grain
- ignoring late/out-of-order events
- no backfill strategy
- no quality checks or alerting
Company archetype adjustments
- FAANG/high-growth: scale + real-time needs often stronger.
- Enterprise: governance, compliance, and integration are heavier.
- Automotive/industrial: lifecycle is long; traceability and reliability are key.