Mid
Build the data foundation that powers Reddit's Consumer product decisions
Reddit is hiring an Analytics Engineer to sit at the intersection of Data Science and Data Engineering within its Consumer Data Science org. You'll own data pipelines, ETLs, and self-service tooling that support product feature tracking, retention analysis, and A/B testing for a product with 120M+ daily active users. This interview focuses on large-scale ETL design, Python/SQL/Spark fluency, and the ability to act as a trusted conduit between data producers and data consumers.
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What this interview tests
- Large-scale ETL design, implementation, and maintenance
- Python, SQL, Spark/Scala programming depth
- Data modeling and governance for consumer product analytics
- Building self-service tools and dashboards, driving adoption
- Cross-functional communication with Data Science, Product, Engineering, Design
Common question themes
Describe an ETL pipeline you built or owned — data scale, architecture, and how you handled reliability.
How do you approach data modeling tradeoffs when producers and consumers have different needs?
Tell me about a self-service tool or dashboard you built — how did you drive adoption?
How have you mentored or guided data scientists/engineers on data foundations?
Walk through a time you had to reconcile messy or inconsistent data for product analysis (A/B testing, retention, etc.).
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