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).
EasyTechnical
0 practiced
List essential data quality checks you would implement for the ride events pipeline to catch issues like time-traveling events, duplicate events, schema changes, and missing GPS coordinates. For each check, specify alert thresholds, remediation strategies, and how you'd automate detection and recovery.
EasyTechnical
0 practiced
As an AI engineer, what minimal telemetry and logging would you require from the ride events ingestion pipeline to support model training data quality and fast root-cause analysis? List at least 8 signals (e.g., event-latency, drop-rates) and explain why each is important.
MediumTechnical
0 practiced
Given frequent GPS pings from drivers and riders, propose a schema and storage strategy that supports efficient geospatial queries such as 'nearest drivers within 2 km' and tile-based heatmaps. Discuss indexing options (H3, geohashes, PostGIS), storage formats (Parquet vs raw), and pre-aggregation strategies for dashboards.
HardTechnical
0 practiced
Assume a model was trained using a label computed from a buggy historical surge schedule. Describe how you would detect potential label leakage or label bias introduced by the bug, quantify impact on model performance and predictions, and remediate models and pipelines to prevent future occurrences.
MediumTechnical
0 practiced
Write a PySpark Structured Streaming snippet (pseudocode is fine) that consumes ride events from Kafka, computes per-driver 1-minute rolling average passenger_count, and writes aggregates to a sink with exactly-once semantics. Explain your watermarking, state management, checkpointing, and fault-tolerance approach.
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