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 Architecture and Tradeoffs
Designing data architectures specifically for cloud environments and evaluating platform trade offs. Topics include when to use managed relational services, managed nonrelational services, cloud data warehouses, cloud object storage, lifecycle policies, cross region replication, data residency and compliance considerations, cost versus performance trade offs, managed service operational constraints, and strategies for high availability and disaster recovery in the cloud. Candidates should be able to compare cloud service options and justify choices based on reliability, cost, and compliance.
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 Pipeline Monitoring and Observability
Focuses on designing monitoring and observability specifically for data pipelines and streaming workflows. Key areas include instrumenting pipeline stages, tracking health and business level metrics such as latency throughput volume and error rates, detecting anomalies and backpressure, ensuring data quality and completeness, implementing lineage and impact analysis for upstream failures, setting service level objectives and alerts for pipeline health, and enabling rapid debugging and recovery using logs metrics traces and lineage data. Also covers tooling choices for pipeline telemetry, alert routing and escalation, and runbooks for operational playbooks.
Cloud Data Processing and Streaming
Design and operate data processing architectures in the cloud for both streaming and batch workloads including extract transform and load pipelines ingestion and messaging patterns partitioning strategies stateful and stateless processing windowing semantics fault tolerance checkpointing and recovery idempotent design back pressure and flow control orchestration and scheduling for batch jobs and cost and performance trade offs. Discuss testing deployment and observability approaches and be ready to reference common tools and managed services for streaming and batch processing and how you ensure correctness latency and scalability in production.
Data Pipeline Architecture
Design end to end data pipeline solutions from problem statement through implementation and operations, integrating ingestion transformation storage serving and consumption layers. Topics include source selection and connectors, ingestion patterns including batch streaming and micro batch, transformation steps such as cleaning enrichment aggregation and filtering, and loading targets such as analytic databases data warehouses data lakes or operational stores. Cover architecture patterns and trade offs including lambda kappa and micro batch, delivery semantics and fault tolerance, partitioning and scaling strategies, schema evolution and data modeling for analytic and operational consumers, and choices driven by freshness latency throughput cost and operational complexity. Operational concerns include orchestration and scheduling, reliability considerations such as error handling retries idempotence and backpressure, monitoring and alerting, deployment and runbook planning, and how components work together as a coherent maintainable system. Interview focus is on turning requirements into concrete architectures, technology selection, and trade off reasoning.