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
Case study: Finance reports a mismatch of $X between billed revenue and revenue in the analytics warehouse for the past month. Outline a systematic reconciliation approach: align metric definitions, compare by day/city, examine cancellations/refunds/chargebacks, check timezone boundaries, and identify ETL or rounding issues. Propose fixes and monitoring to prevent recurrence.
MediumTechnical
0 practiced
Explain how you'd implement end-to-end lineage for a derived ML feature 'avg_trip_duration_7d' used in an alerting system. Describe which metadata to capture (raw event source, transformation code and version, intermediate tables, pipeline run id), tools or stores to use, and what queries or UI features you'd provide to make root-cause analysis straightforward.
HardSystem Design
0 practiced
Design a privacy-compliant audit trail for data access and transformations in Lyft's analytics platform. Requirements: immutable logs, who-accessed-what dataset or derived feature, transformation lineage, timestamps, purpose of access, retention policies, and ability to produce reports for compliance audits. Include storage, indexing, and access control considerations.
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.
MediumTechnical
0 practiced
A product manager requests 'average pickup time' for a city launch in 3 days but data engineering backlog is full. How would you prioritize, propose a pragmatic approach (approximate vs exact), outline a minimal deliverable, and communicate trade-offs and timelines to stakeholders?
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