A user asks to delete all their personal data. Your data is spread across a data lake, warehouse and backups. How do you handle it?

What the interviewer wants to hear

• Know where PII lives using a catalog and lineage
• Delete or anonymise in mutable table formats with row-level deletes
• Vacuum or expire old snapshots so data is physically removed
• Pseudonymisation or crypto-shredding to make deletion easier
• Handle backups with retention policies
• Log and verify completion within the legal deadline

Model answer

First, you need to know where personal data lives, so a catalog with PII tags and lineage is essential. Deletion requests go into a queue with the user ID and a deadline, and a job processes them across systems. In the warehouse, a DELETE or MERGE removes rows. In the lake, plain Parquet files are immutable, so table formats like Delta Lake or Iceberg help: they support row-level deletes, but old snapshots still hold the data until you vacuum or expire them, so that step is mandatory. A good design keeps PII in a few tables keyed by a pseudonymous ID, so most tables never store names or emails. Crypto-shredding goes further: encrypt each user's data with their own key and delete the key. Backups follow a fixed retention, after which deleted data ages out. Every request is logged and verified, and laws like GDPR or India's DPDP Act set the deadlines.

More hard Data Engineer questions

• Design a data platform for a fast-growing startup with 20 data sources and a small team. What would you choose and why?
• Explain at-most-once, at-least-once and exactly-once processing, and how you achieve effectively-once results in practice.
• How do you choose partitioning and clustering for a very large table? What can go wrong?
• The CEO's revenue dashboard showed wrong numbers for two days before anyone noticed. How do you fix this and prevent it from happening again?
• Design a pipeline that ingests 200,000 clickstream events per second and powers both a real-time dashboard and daily analytics.
• You must migrate a company from an on-premise warehouse to Snowflake or BigQuery without breaking hundreds of reports. How would you plan it?
• Leadership asks you to cut the cloud warehouse bill by 40 percent without hurting the business. What do you do?
• A metric definition changed and you must backfill two years of a large fact table while the daily pipeline keeps running. How do you do it safely?
• Design a CDC pipeline that replicates a busy PostgreSQL database into Iceberg or Delta tables, including updates and deletes. What are the tricky parts?