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.
Cloud Data Warehouse Architecture
Understand modern cloud data platforms: Snowflake, BigQuery, Redshift, Azure Synapse. Know their architecture, scalability models, performance characteristics, and cost optimization strategies. Discuss separation of compute and storage, time travel, and zero-copy cloning.
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 Lake and Warehouse Architecture
Designing scalable data platforms for analytical and reporting workloads including data lakes, data warehouses, and lakehouse architectures. Key topics include storage formats and layout including columnar file formats such as Parquet and table formats such as Iceberg and Delta Lake, partitioning and compaction strategies, metadata management and cataloging, schema evolution and transactional guarantees for analytical data, and cost and performance trade offs. Cover ingestion patterns for batch and streaming data including change data capture, data transformation approaches and compute engines for analytical queries, partition pruning and predicate pushdown, query optimization and materialized views, data modeling for analytical workloads, retention and tiering, security and access control, data governance and lineage, and integration with business intelligence and real time analytics. Also discuss operational concerns such as monitoring, vacuuming and compaction jobs, metadata scaling, and strategies for minimizing query latency while controlling storage cost.
Analytics Architecture and Reporting
Designing and operating end to end analytics and reporting platforms that translate business requirements into reliable and actionable insights. This includes defining metrics and key performance indicators for different audiences, instrumentation and event design for accurate measurement, data ingestion and transformation pipelines, and data warehouse and storage architecture choices. Candidates should be able to discuss data modeling for analytics including semantic layers and data marts, approaches to ensure metric consistency across tools such as a single source of truth or metric registry, and trade offs between query performance and freshness including batch versus streaming approaches. The topic also covers dashboard architecture and visualization best practices, precomputation and aggregation strategies for performance, self service analytics enablement and adoption, support for ad hoc analysis and real time reporting, plus access controls, data governance, monitoring, data quality controls, and operational practices for scaling, maintainability, and incident detection and resolution. Interviewers will probe end to end implementations, how monitoring and quality controls were applied, and how stakeholder needs were balanced with platform constraints.
Data Transformation and Preparation
Focuses on the technical skills and judgement required to connect to data sources, clean and shape data, and prepare datasets for analysis and visualization. Includes identifying necessary transformations such as calculations, aggregations, filtering, joins, and type conversions; deciding whether to perform transformations in the business intelligence tool or in the data warehouse or database layer; designing efficient data models and extract transform load workflows; ensuring data quality, lineage, and freshness; applying performance optimization techniques such as incremental refresh and pushdown processing; and familiarity with tools and features such as Power BI Power Query, Tableau data preparation capabilities, and structured query language for database level transformations. Also covers documentation, reproducibility, and testing of data preparation pipelines.
Data Warehousing and Data Lakes
Covers conceptual and practical design, architecture, and operational considerations for data warehouses and data lakes. Topics include differences between warehouses and lakes, staging areas and ingestion patterns, schema design such as star schema and dimensional modeling, handling slowly changing dimensions and fact tables, partitioning and bucketing strategies for large datasets, common architectures including medallion architecture with bronze silver and gold layers, real time and batch ingestion approaches, metadata management, and data governance. Interview questions may probe trade offs between architectures, how to design schemas for analytical queries, how to support both analytical performance and flexibility, and how to incorporate lineage and governance into designs.
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.
Automated Reporting & Report Development
Build automated reports that refresh on schedule. Understand refresh schedules, data pipeline integration, and deployment to production. Create parameterized reports for different stakeholder needs. Know how to version control and manage report changes.