Data engineering preparation: pipelines, keys and recovery

Define the data contract

Name the source, expected schema, record key, event time and update behaviour. Agree the destination grain and acceptable delay. A daily report and an event-driven application can require different architectures.

Practise a safe load

Design one repeatable batch before adding scale. Consider duplicate source records, late updates, partial failures and schema changes. Demonstrate that retrying the same batch does not duplicate destination records.

Work through safe pipeline retries →

Validate the result

• Check required keys and fields.
• Reconcile records and amounts for a known source interval.
• Track freshness and unexpected volume changes.
• Keep failed validation visible and assign an owner.
• Explain what a successful job status does not prove.

Practise reconciliation →

Choose tool depth deliberately

Learn SQL and a programming language suited to the job. Then examine the orchestration, warehouse and cloud tools actually named in the role. No single vendor is mandatory for every senior position. Explain a tool’s role and limitations rather than reciting product names.

Use technical-round preparation, SQL and Python.

A system-design practice prompt

A daily orders report is missing updates after a retry. Sketch the source-to-report flow, identify where duplicates or omissions could occur, and propose checks. State the consistency, latency and cost tradeoffs. Avoid choosing a distributed system before establishing the scale.