Situation: At my previous company, analytics teams requested "better customer lifecycle data" but gave vague requirements—no schema, freshness, or consumers defined. The backlog had a single ticket: “improve lifecycle metrics,” which blocked downstream modeling.
Task: As the data engineer owner, I needed to turn ambiguity into a concrete, deliverable data product: a reliable, documented customer_lifecycle table that served analytics and ML teams.
Action:
- Elicited requirements via stakeholder interviews (2x product managers, 3x analysts, 1 ML engineer). I asked targeted questions: which events matter, acceptable latency, primary consumers, SLAs, privacy constraints.
- Consolidated answers into a short spec: canonical customer_id, event types (signup, purchase, churn), event timestamps, derived lifetime_value, daily freshness < 4 hours, row-level PII hashing.
- Defined success metrics: data completeness > 99%, mean freshness < 4 hours, <1% schema change failures, and adoption metric (>=3 teams using within 8 weeks).
- Broke work into an MVP and milestones:
- MVP (2 sprints): ingest core events into Snowflake, implement Airflow pipeline + Spark transforms, basic docs and tests, monitor for completeness.
- Milestone 1 (next month): add LTV calculation via dbt, versioned schema, lineage in Data Catalog.
- Milestone 2: add customer segmentation attributes, incremental CDC ingestion, automated alerting.
- Validated assumptions with stakeholders via weekly demos and an acceptance checklist; built a lightweight contract (schema + SLA) they signed off on before production run.
Result: Delivered MVP in 4 weeks. Post-launch: completeness = 99.6%, mean freshness = 2.8 hours, and 4 teams adopted the table within 6 weeks, reducing ad-hoc ETL requests by ~60%. The iterative milestones allowed us to add features without breaking consumers. This taught me the value of focused interviews, measurable SLAs, and small, validated releases when converting fuzzy product asks into reliable data products.
