I started with a star schema and talked through fact tables for user activity, connections, job applications, that kind of thing.
Start by clarifying the business goals and key analytical questions LinkedIn needs to answer, then propose a dimensional model with a central fact table (e.g., member activity) and conformed dimensions (member, company, job, time). Discuss trade-offs between star and snowflake schemas, and how to handle slowly changing dimensions and scalability for billions of events.
Pro tip: Emphasize the importance of partitioning and clustering strategies for big data (e.g., by date and member ID) to optimize query performance and cost, and mention how you would handle late-arriving data and data quality checks.
Ask about the primary use cases (e.g., member engagement, ad performance, talent insights) and the scale of data (billions of events per day). Identify key metrics and dimensions needed.
Choose a grain (e.g., one row per member action per day) and define additive measures (e.g., clicks, impressions, connections made). Consider partitioning by date and clustering by member ID for performance.
Create conformed dimensions: member (with SCD Type 2 for profile changes), company, job, time, and location. Ensure they are denormalized for query simplicity.
Discuss star vs. snowflake schema, normalization vs. performance, and batch vs. streaming ingestion. Explain how you would handle slowly changing dimensions and late-arriving facts.
Propose partitioning, indexing, and clustering strategies. Mention data governance, ETL/ELT pipelines, and how to handle schema evolution and data quality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.