Design a data platform for a fast-growing startup with 20 data sources and a small team. What would you choose and why?

What the interviewer wants to hear

• Prefer managed services to reduce operations load
• ELT with managed connectors into a cloud warehouse or lakehouse
• dbt for transformations with layered models
• Orchestration, testing and observability
• Governance: access control, PII handling, documentation
• Plan for cost control and future streaming needs

Model answer

With a small team, I would minimise what we must operate ourselves. Ingestion uses managed connectors like Fivetran or Airbyte Cloud for SaaS sources, and CDC for the main databases. Everything lands raw in one cloud warehouse such as Snowflake or BigQuery, or a lakehouse if we expect heavy ML work. Transformations live in dbt with three layers: staging for cleaning, intermediate for business logic, and marts for BI, all in Git with code review and CI that runs tests on changed models. A managed orchestrator, or dbt Cloud plus a simple Airflow, schedules runs. I add data tests, freshness checks and alerts from day one. For governance, role-based access, masking of PII columns, and a data catalog from dbt docs. I set budgets and warehouse auto-suspend to control cost. Streaming waits until a real use case appears.

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