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

What is a Data Protection Impact Assessment (DPIA)? Describe when a DPIA should be conducted for a new product feature, what core sections it should include (data flows, risks, mitigations, residual risk), and how engineers should integrate DPIA findings into implementation plans.

MediumTechnical

0 practiced

How would you integrate privacy checks into CI/CD to detect risky code changes such as accidental logging of PII, inclusion of suspicious third-party libraries, or missing consent checks? Describe static/dynamic checks, policy-as-code, gating strategies, and how to handle false positives.

EasyTechnical

0 practiced

Define data minimization and suggest five concrete engineering steps you would take while designing and implementing a new feature to minimize the collection and retention of personal data.

HardSystem Design

0 practiced

Design a privacy-preserving analytics platform that supports ad-hoc aggregations and dashboards for 100M users. Requirements: apply differential privacy to protect individual contributions, manage privacy budgets (epsilon), support batch and near-real-time use cases, and provide guidance to analysts about DP-degraded results.

MediumTechnical

0 practiced

A feature proposes using hashed email addresses to join data across services for personalization. Evaluate whether simple unsalted hashing is sufficient for privacy/compliance, list practical threats (rainbow tables, re-identification), and propose stronger alternatives such as keyed HMACs, per-purpose tokens, or centralized tokenization service.

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