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
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Given an events table:Write a SQL query to compute the cancellation rate per city for trips requested between '2024-02-01' and '2024-02-28'. A trip is considered cancelled if a CANCELED event exists for its trip_id before any STARTED event. Handle duplicate events by using event_time ordering and window functions.
events(event_id STRING, trip_id STRING, event_type STRING, event_time TIMESTAMP, city STRING, driver_id STRING, rider_id STRING)HardTechnical
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Late-arriving or out-of-order events can affect billing and driver earnings. Propose a system design and reconciliation strategy that provides low-latency provisional analytics while guaranteeing accurate finalized billing. Discuss windowing, provisional vs finalized pipelines, compensation transactions, idempotency, and auditability.
MediumTechnical
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Describe how you would design a feature store for Lyft that supports: offline feature computation for training, low-latency online serving for pricing/ETA models, feature discoverability and metadata, freshness guarantees, and consistent joins on high-cardinality keys (driver_id). Include storage choices and ingestion approaches.
MediumTechnical
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Compare Kafka, AWS Kinesis, and cloud pub/sub systems for ingesting Lyft ride events at scale. Discuss throughput, ordering guarantees, exactly-once capabilities, operational complexity, multi-region replication, and ecosystem tooling (connectors, stream processors). Which factors would most influence your choice?
HardTechnical
0 practiced
A query that computes rolling 7-day average trips per user is too slow on petabyte-scale data. Describe a set of optimizations: materialized daily aggregates, using window functions on pre-aggregated data, partition/prune strategies, using incremental updates/materialized views, and approximate techniques. Provide a high-level SQL rewrite or steps you would take to optimize the query.
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