Data Engineering & Analytics Infrastructure Topics
Data pipeline design, ETL/ELT processes, streaming architectures, data warehousing infrastructure, analytics platform design, and real-time data processing. Covers event-driven systems, batch and streaming trade-offs, data quality and governance at scale, schema design for analytics, and infrastructure for big data processing. Distinct from Data Science & Analytics (which focuses on statistical analysis and insights) and from Cloud & Infrastructure (platform-focused rather than data-flow focused).
Data Collection and Instrumentation
Designing and implementing reliable data collection and the supporting data infrastructure to power analytics and machine learning. Covers event tracking and instrumentation design, decisions about what events to log and schema granularity, data validation and quality controls at collection time, sampling and deduplication strategies, attribution and measurement challenges, and trade offs between data richness and cost. Includes pipeline and ingestion patterns for real time and batch processing, scalability and maintainability of pipelines, backfill and replay strategies, storage and retention trade offs, retention policy design, anomaly detection and monitoring, and operational cost and complexity of measurement systems. Also covers privacy and compliance considerations and privacy preserving techniques, governance frameworks, ownership models, and senior level architecture and operationalization decisions.
Metric Definition and Implementation
End to end topic covering the precise definition, computation, transformation, implementation, validation, documentation, and monitoring of business metrics. Candidates should demonstrate how to translate business requirements into reproducible metric definitions and formulas, choose aggregation methods and time windows, set filtering and deduplication rules, convert event level data to user level metrics, and compute cohorts, retention, attribution, and incremental impact. The work includes data transformation skills such as normalizing and formatting date and identifier fields, handling null values and edge cases, creating calculated fields and measures, combining and grouping tables at appropriate levels, and choosing between percentages and absolute numbers. Implementation details include writing reliable structured query language code or scripts, selecting instrumentation and data sources, considering aggregation strategy, sampling and margin of error, and ensuring pipelines produce reproducible results. Validation and quality practices include spot checks, comparison to known totals, automated tests, monitoring and alerting, naming conventions and versioning, and clear documentation so all calculations are auditable and maintainable.
Analytics Platforms and Dashboards
Comprehensive knowledge of analytics platforms, implementation of tracking, reporting infrastructure, and dashboard design to support marketing, product, and content decisions. Candidates should be able to describe tool selection and configuration for platforms such as Google Analytics Four, Adobe Analytics, Mixpanel, Amplitude, Tableau, and Looker, including the trade offs between vendor solutions, native platform analytics, and custom instrumentation. Core implementation topics include defining measurement plans and event schemas, event instrumentation across web and mobile, tagging strategy and data layer design, Urchin Tracking Module parameter handling and cross domain attribution, conversion measurement, and attribution model design. Analysis and reporting topics include funnel analysis, cohort analysis, retention and segmentation, key performance indicator definition, scheduled reporting and automated reporting pipelines, alerting for data anomalies, and translating raw metrics into stakeholder ready dashboards and narrative visualizations. Integration and governance topics include data quality checks and validation, data governance and ownership, exporting and integrating analytics with data warehouses and business intelligence pipelines, and monitoring instrumentation coverage and regression. The scope also covers channel specific analytics such as search engine optimization tools, social media native analytics, and email marketing metrics including delivery rates, open rates, and click through rates. For junior candidates, demonstration of fluency with one or two tools and basic measurement concepts is sufficient; for senior candidates, expect discussion of architecture, pipeline automation, governance, cross functional collaboration, and how analytics drive experiments and business decisions.
Lyft-Specific Data Modeling & Analytics Requirements
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).