Company Privacy Landscape Questions

Demonstrate company specific understanding of privacy and data protection considerations. This covers the organization public privacy commitments, data handling scale and types, major privacy initiatives, known privacy risks or incidents, applicable privacy regulations for their markets and products, data governance practices, and how privacy requirements influence product design, analytics, and third party integrations. Interviewers look for evidence you researched the company privacy context and can discuss implications for compliance, user trust, and practical privacy engineering or policy tradeoffs.

EasyTechnical

0 practiced

Explain the privacy-by-design principles of purpose limitation and data minimization. Give two specific examples of how those principles would change the design of an ETL pipeline (ingestion schema, storage decisions, or transformations).

MediumTechnical

0 practiced

For big data systems like Hive, BigQuery, and Snowflake, describe what audit logging you would capture for data access and modifications to support investigation and compliance. Include retention strategy, tamper-evidence, indexing for search, and integration with SIEM or compliance dashboards.

MediumTechnical

0 practiced

You discover an analytics pipeline has been ingesting raw email addresses into the events topic for several months. Outline a remediation plan: detection (how to quantify scope), immediate mitigations, long-term fixes, and communication with privacy/legal stakeholders. Include trade-offs between masking, deletion, and notifying affected users.

HardTechnical

0 practiced

You're asked to implement differential privacy across an existing Spark batch analytics pipeline that computes many metrics over user cohorts. Discuss specific algorithmic choices (Laplace vs Gaussian), how to implement per-user contribution bounding in a distributed environment, how to track and manage an overall epsilon budget, and how to test and validate the implementation.

MediumTechnical

0 practiced

Write pseudocode or SQL for a stored procedure that deletes a user's data across a normalized relational schema (tables: users, orders, sessions, events) while preserving referential integrity, ensuring idempotency, and writing an audit record for the deletion that includes timestamp, operator, and affected tables.

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