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 Architecture and Pipelines
Designing data storage, integration, and processing architectures. Topics include relational and NoSQL database design, indexing and query optimization, replication and sharding strategies, data warehousing and dimensional modeling, ETL and ELT patterns, batch and streaming ingestion, processing frameworks, feature stores, archival and retention strategies, and trade offs for scale and latency in large data systems.
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 Technical Strategy Alignment
Assess how the candidates technical experience and perspective align with the companys data strategy, infrastructure, and product architecture. Candidates should demonstrate knowledge of the companys scale, data driven products, and technical tradeoffs, and then explain concretely how their past work, tools, and approaches would support the companys data objectives. Good answers connect specific technical skills and project outcomes to the companys announced or inferred data and engineering priorities.
Distributed Data Processing and Optimization
Comprehensive knowledge of processing large datasets across a cluster and practical techniques for optimizing end to end data pipelines in frameworks such as Apache Spark. Candidates should understand distributed computation patterns such as MapReduce and embarrassingly parallel workloads, how work is partitioned across tasks and executors, and how partitioning strategies affect data locality and performance. They should explain how and when data shuffles occur, why shuffles are expensive, and how to minimize shuffle cost using narrow transformations, careful use of repartition and coalesce, broadcast joins for small lookup tables, and map side join approaches. Coverage should include join strategies and broadcast variables, avoiding wide transformations, caching versus persistence trade offs, handling data skew with salting and repartitioning, and selecting effective partition keys. Resource management and tuning topics include executor memory and overhead, cores per executor, degree of parallelism, number of partitions, task sizing, and trade offs between processing speed and resource usage. Fault tolerance and scaling topics include checkpointing, persistence for recovery, and strategies for horizontal scaling. Candidates should also demonstrate monitoring, debugging, and profiling skills using the framework user interface and logs to diagnose shuffles, stragglers, and skew, and to propose actionable tuning changes and coding patterns that scale in distributed environments.
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.
Data Pipelines and Feature Platforms
Designing and operating data pipelines and feature platforms involves engineering reliable, scalable systems that convert raw data into production ready features and deliver those features to both training and inference environments. Candidates should be able to discuss batch and streaming ingestion architectures, distributed processing approaches using systems such as Apache Spark and streaming engines, and orchestration patterns using workflow engines. Core topics include schema management and evolution, data validation and data quality monitoring, handling event time semantics and operational challenges such as late arriving data and data skew, stateful stream processing, windowing and watermarking, and strategies for idempotent and fault tolerant processing. The role of feature stores and feature platforms includes feature definition management, feature versioning, point in time correctness, consistency between training and serving, online low latency feature retrieval, offline materialization and backfilling, and trade offs between real time and offline computation. Feature engineering strategies, detection and mitigation of distribution shift, dataset versioning, metadata and discoverability, governance and compliance, and lineage and reproducibility are important areas. For senior and staff level candidates, design considerations expand to multi tenant platform architecture, platform application programming interfaces and onboarding, access control, resource management and cost optimization, scaling and partitioning strategies, caching and hot key mitigation, monitoring and observability including service level objectives, testing and continuous integration and continuous delivery for data pipelines, and operational practices for supporting hundreds of models across teams.
Data Ingestion Strategies and Tools
Covers patterns, approaches, and technologies for moving data from source systems into downstream storage and processing platforms. Candidates should understand pull based and push based ingestion models including periodic polling of application interfaces, event driven webhooks, log collection, file based batch uploads, database replication using change data capture, and streaming ingestion. Evaluate trade offs for latency, throughput, ordering, delivery semantics such as at least once and exactly once, backpressure and flow control, idempotency, fault tolerance, and cost. Be familiar with common ingestion technologies and platforms such as Apache Kafka, Amazon Kinesis, Google PubSub, and Apache NiFi as well as managed cloud ingestion and extract transform load services. Topics also include schema management and evolution, data formats such as JavaScript Object Notation and columnar file formats, data validation and cleansing at ingress, security and authentication for ingestion pipelines, monitoring and observability, and operational concerns for scaling and recovery.
Data Quality and Validation
Covers the core concepts and hands on techniques for detecting, diagnosing, and preventing data quality problems. Topics include common data issues such as missing values, duplicates, outliers, incorrect labels, inconsistent formats, schema mismatches, referential integrity violations, and distribution or temporal drift. Candidates should be able to design and implement validation checks and data profiling queries, including schema validation, column level constraints, aggregate checks, distinct counts, null and outlier detection, and business logic tests. This topic also covers the mindset of data validation and exploration: how to approach unfamiliar datasets, validate calculations against sources, document quality rules, decide remediation strategies such as imputation quarantine or alerting, and communicate data limitations to stakeholders.