Lyft-Specific Data Modeling & Analytics Requirements Questions
Lyft-specific data modeling and analytics requirements for data platforms, including ride event data, trip-level schemas, driver and rider dimensions, pricing and surge data, geospatial/location data, and analytics needs such as reporting, dashboards, and real-time analytics. Covers analytic schema design (star/snowflake), ETL/ELT patterns, data quality and governance at scale, data lineage, privacy considerations, and integration with the broader data stack (data lake/warehouse, streaming pipelines).
MediumTechnical
0 practiced
Surge multipliers change frequently and sometimes are applied retroactively. Propose a storage and schema strategy that allows analytics to compute revenue using the surge multiplier that was effective at the ride time and also supports retroactive corrections. Include temporal table structure, efficient join strategies, and suggestions for denormalizing at write-time vs joining at query-time.
HardTechnical
0 practiced
Two dashboards report different daily revenue numbers. Describe a prioritized, reproducible methodology to find the root cause using lineage, dataset versions, sampling raw events, backfills/replays, and validation queries. Explain how you'd communicate findings to stakeholders and decide whether to roll back changes or accept corrected numbers.
MediumTechnical
0 practiced
Describe options to implement end-to-end data lineage at Lyft: instrumenting ETL jobs, integrating with OpenLineage/Marquez, capturing dataset versions, and surfacing lineage in a catalog. Explain how lineage helps debug metric discrepancies and how you'd implement field-level lineage for critical metrics.
MediumTechnical
0 practiced
Compare batch ETL and ELT approaches for computing Lyft's daily revenue and driver payments. Discuss data freshness, compute cost, operational complexity, and tools commonly used for each. Recommend which approach fits best under what constraints.
HardSystem Design
0 practiced
Design a feature extraction and serving pipeline to support personalized driver incentives analytics that requires joining real-time events with historical behavior and ML scores. Specify feature freshness SLAs, storage for online features, streaming vs batch compute for features, and how to ensure feature consistency between training and serving.
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