Design a GDPR data deletion pipeline
PremiumData pipelineHard
- GDPR
- Right to erasure
- Crypto shredding
Quick answer
This data pipeline interview question asks you to design a GDPR data deletion pipeline. Aim for a design that moves data from sources to consumers reliably and on time, then explain ingestion, processing, storage and trade-offs. Interviewers want to see how you handle volume, late or bad data and failures along the way.
The problem
Design a GDPR data deletion pipeline: when a user asks to be forgotten, it finds and erases their personal data across 180 data stores, the data lake, search indexes, caches, backups and third-party processors, then keeps proof. The hard parts are knowing where data lives, immutable files and backups, and legal holds.
The GDPR right to erasure requires a company to act without undue delay, normally within one month. Missing one forgotten table is a legal risk, and so is deleting records the law says to keep, such as invoices.
What must a GDPR data deletion pipeline do?
- Fan-out: Send each request to every system that holds the user's data, and track each system's status.
- Inventory: Know every store that holds personal data, which columns, and how to find one user's rows.
How well must it do it?
- Deadline: 99.9% of requests complete within 14 days; none exceed 30 days.
- Completeness: A request closes only when every system confirms or is marked exempt with a reason.
What scale should you design for?
Use these numbers in your estimates.
The scale numbers unlock with this questionUnlock
Key concepts
- GDPR
- Right to erasure
- Crypto shredding
- Personal data inventory
- Deletion orchestration
- Apache iceberg
How you’ll practise
- Requirements
- Data volume
- Ingestion
- Storage and processing
- Serving and freshness
- Wrap-up
Lala, personalized to you, gives a hint when you ask and reviews your design when you are ready.
What do interviewers look for?
In any data pipeline design round.
- Clarifies sources, consumers and freshness needs
- Chooses batch, streaming or both with reasons
- Handles late, duplicate and malformed records
- Makes each stage safe to retry
- Explains storage formats and partitioning
- Plans monitoring, backfills and data quality checks
Questions people ask about this interview question
- Which design type is this question?
- It is a data pipeline question. You design a GDPR data deletion pipeline and explain your choices the way you would in an interview.
- Is this question asked at entry level?
- It is marked Hard. It suits engineers preparing for senior interviews, where you are expected to drive the discussion and go deep on trade-offs.
- What should I cover first?
- Start with the data sources, the consumers and how fresh the data must be. Agree on scope with the interviewer before you design anything.
- Can I practise it for free?
- It is a premium question. Sign up and unlock it once for 10 credits; it then stays unlocked. When you finish, Lala, your personalized AI mentor, can review your design for 30 credits.
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Updated · Written by the SevyDevy team