DoorDash·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

DoorDash analytics engineer interview that was basically one long deep-dive on a single ETL pipeline you've built. They give you around 40 minutes and peel back every layer, so if you've only ever touched one part of a pipeline end-to-end, this round will expose that fast.

Questions Asked (1)

Walk me through an ETL pipeline you built end-to-end, covering source systems and ingestion, schema and transformation logic, orchestration and scheduling, data quality checks and SLAs, monitoring and alerting, failure handling and backfills, and the trade-offs you made.

System DesignTechnical Trade-offsData Modeling

Author's notes

This is the whole interview, not just an opener.

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AI HintsAI Generated

Suggested Approach

Choose a real ETL pipeline you built and narrate it as a story with clear stages: source, ingestion, transformation, orchestration, quality, monitoring, and failure handling. Highlight trade-offs and decisions you made, tying them to business impact and SLAs. Keep it structured and concise, focusing on what you did and why.

Pro tip: Quantify the impact of your pipeline (e.g., reduced latency by X%, improved data accuracy to Y%) and mention how you involved stakeholders to define SLAs. This shows you understand both technical and business aspects.

1. Set the context

Briefly describe the business problem, data sources, and the goal of the ETL pipeline. Mention the scale (data volume, frequency) and SLAs.

2. Walk through the pipeline stages

Explain ingestion (batch/streaming, tools), schema design, transformation logic (cleansing, enrichment, aggregation), and orchestration (scheduling, dependencies).

3. Cover data quality and monitoring

Describe the data quality checks you implemented (e.g., completeness, uniqueness, freshness) and how you monitored pipeline health and alerted on failures.

4. Discuss failure handling and backfills

Explain how you handled failures (retries, dead-letter queues) and backfilled data (idempotency, reprocessing).

5. Highlight trade-offs and outcomes

Summarize key trade-offs (e.g., latency vs. cost, simplicity vs. flexibility) and the resulting impact on the business.

Key Points to Mention

Source systems and ingestion patterns (e.g., Kafka, CDC, batch APIs)
Schema evolution and data modeling choices (e.g., star schema, normalization)
Orchestration tools (e.g., Airflow, Dagster) and scheduling strategies
Data quality frameworks and SLA definitions
Monitoring and alerting tools (e.g., Prometheus, Grafana, PagerDuty)
Failure handling mechanisms (e.g., retries, idempotency, backfill processes)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.