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).
Geospatial and Real Time Processing
Covers design and operation of systems that handle spatial data and low latency event streams. Candidates should explain spatial indexing and query techniques, map matching and coordinate reference considerations, spatial accuracy and privacy trade offs, and storage approaches for geospatial data. For real time processing describe ingestion, messaging patterns, stream processing concepts such as windowing and stateful processing, ordering and delivery semantics, partitioning and scaling strategies, backpressure and fault handling, and trade offs between real time and batch analytics for customer facing metrics.
Google Cloud Data Services
Covers design and operational knowledge of Google Cloud Platform data products used for storage, processing, streaming, and analytics. Key skills include when and how to use BigQuery for serverless analytics and data warehousing, Dataflow for stream and batch pipelines built on Apache Beam, Cloud Storage for object store and data lake patterns, and Pub/Sub for messaging and event ingestion. Candidates should understand cost models, performance trade offs, schema and partitioning strategies, data ingestion and export patterns, pipeline monitoring and error handling, and integration between these services for end to end data solutions.
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).
Stream Processing and Event Streaming
Designing and operating systems that ingest, process, and serve continuous event streams with low latency and high throughput. Core areas include architecture patterns for stream native and event driven systems, trade offs between batch and streaming models, and event sourcing concepts. Candidates should demonstrate knowledge of messaging and ingestion layers, message brokers and commit log systems, partitioning and consumer group patterns, partition key selection, ordering guarantees, retention and compaction strategies, and deduplication techniques. Processing concerns include stream processing engines, state stores, stateful processing, checkpointing and fault recovery, processing guarantees such as at least once and exactly once semantics, idempotence, and time semantics including event time versus processing time, watermarks, windowing strategies, late and out of order event handling, and stream to stream and stream to table joins and aggregations over windows. Performance and operational topics cover partitioning and scaling strategies, backpressure and flow control, latency versus throughput trade offs, resource isolation, monitoring and alerting, testing strategies for streaming pipelines, schema evolution and compatibility, idempotent sinks, persistent storage choices for state and checkpoints, and operational metrics such as stream lag. Familiarity with concrete technologies and frameworks is expected when discussing designs and trade offs, for example Apache Kafka, Kafka Streams, Apache Flink, Spark Structured Streaming, Amazon Kinesis, and common serialization formats such as Avro, Protocol Buffers, and JSON.
Data and Artificial Intelligence Concepts
Core data engineering and analytics concepts combined with artificial intelligence and machine learning fundamentals relevant to sales engineering. Topics include data modeling, data warehouse and data lake trade offs, batch versus real time processing, streaming and event driven pipelines, extract transform load and extract load transform approaches, analytics and reporting patterns, key performance indicators and metric design, and how these decisions affect latency cost and accuracy. For machine learning candidates should be able to explain model training validation and inference, feature engineering, model deployment and monitoring, machine learning operations and governance, and how to translate model capabilities into business impact for customers. Interviewers also assess the candidate's ability to frame technical trade offs and risks in customer friendly terms and to build simple outcome oriented narratives and metrics.