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).
HardTechnical
0 practiced
Discuss strategies to scale ML feature computation for high-cardinality keys (e.g., driver_id) in near-real-time: incremental aggregation, sharded state stores, TTL and compaction, approximate sketches, checkpointing, and trade-offs between exactly-once and at-least-once semantics with idempotent writes.
EasyTechnical
0 practiced
You need to share aggregated trip datasets with an external partner but must reduce re-identification risk. Propose an anonymization strategy that preserves analytic utility (zone-level trip counts, peak times) while protecting riders: spatial and temporal bucketing, count thresholds, Laplace noise addition, and suppression of rare events. Discuss trade-offs.
HardTechnical
0 practiced
Describe how to implement deduplication and idempotent writes in Spark Structured Streaming when ingesting trip events to ensure correctness under at-least-once delivery. Discuss watermarks, stateful dedup using event_id, TTL-based cleanup, and sinks that support atomic upserts or transactions.
HardSystem Design
0 practiced
Design a metadata and lineage model that enables tracing a production model feature back to raw events, transformation code versions, and the pipeline run that produced it. Include how you would capture schema versions, sample hashes, dataset snapshots, and automated impact analysis to quickly identify causes of feature drift or data-quality issues.
EasyTechnical
0 practiced
Given trip-level fields start_time, end_time, start_lat, start_lon, end_lat, end_lon, describe how to compute: 1) trip duration, 2) straight-line distance, and 3) an approximation of route distance. Mention Python libraries you'd use and common pitfalls such as negative durations, missing points, and coordinate anomalies.
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