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.
Batch and Stream Processing
Covers design and implementation of data processing using batch, stream, or hybrid approaches. Candidates should be able to explain when to choose batch versus streaming based on latency, throughput, cost, data volume, and business requirements, and compare architectural patterns such as lambda and kappa. Core stream concepts include event time versus processing time, windowing strategies such as tumbling sliding and session windows, watermarks and late arrivals, event ordering and out of order data handling, stateful versus stateless processing, state management and checkpointing, and delivery semantics including exactly once and at least once. Also includes knowledge of streaming and batch engines and runtimes, connector patterns for sources and sinks, partitioning and scaling strategies, backpressure and flow control, idempotency and deduplication techniques, testing and replayability, monitoring and alerting, and integration with storage layers such as data lakes and data warehouses. Interview focus is on reasoning about correctness latency cost and operational complexity and on concrete architecture and tooling choices.
Data Pipeline Scalability and Performance
Design data pipelines that meet throughput and latency targets at large scale. Topics include capacity planning, partitioning and sharding strategies, parallelism and concurrency, batching and windowing trade offs, network and I O bottlenecks, replication and load balancing, resource isolation, autoscaling patterns, and techniques for maintaining performance as data volume grows by orders of magnitude. Include approaches for benchmarking, backpressure management, cost versus performance trade offs, and strategies to avoid hot spots.
Data Reliability and Fault Tolerance
Design and operate data pipelines and stream processing systems to guarantee correctness, durability, and predictable recovery under partial failures, network partitions, and node crashes. Topics include delivery semantics such as at most once, at least once, and exactly once and the trade offs among latency, throughput, and complexity. Candidates should understand idempotent processing, deduplication techniques using unique identifiers or sequence numbers, transactional and atomic write strategies, and coordinator based or two phase commit approaches when appropriate. State management topics include checkpointing, snapshotting, write ahead logs, consistent snapshots for aggregations and joins, recovery of operator state, and handling out of order events. Operational practices include safe retries, retry and circuit breaker patterns for downstream dependencies, dead letter queues and reconciliation processes, strategies for replay and backfill, runbooks and automation for incident response, and failure mode testing and chaos experiments. Data correctness topics include validation and data quality checks, schema evolution and compatibility strategies, lineage and provenance, and approaches to detect and remediate data corruption and schema drift. Observability topics cover metrics, logs, tracing, alerting for pipeline health and state integrity, and designing alerts and dashboards to detect and diagnose processing errors. The topic also includes reasoning about when exactly once semantics are achievable versus when at least once with compensating actions or idempotent sinks is preferable given operational and performance trade offs.
Data Quality and Edge Case Handling
Practical skills and best practices for recognizing, preventing, and resolving real world data quality problems and edge cases in queries, analyses, and production data pipelines. Core areas include handling missing and null values, empty and single row result sets, duplicate records and deduplication strategies, outliers and distributional assumptions, data type mismatches and inconsistent formatting, canonicalization and normalization of identifiers and addresses, time zone and daylight saving time handling, null propagation in joins, and guarding against division by zero and other runtime anomalies. It also covers merging partial or inconsistent records from multiple sources, attribution and aggregation edge cases, group by and window function corner cases, performance and correctness trade offs at scale, designing robust queries and pipeline validations, implementing sanity checks and test datasets, and documenting data limitations and assumptions. At senior levels this expands to proactively designing automated data quality checks, monitoring and alerting for anomalies, defining remediation workflows, communicating trade offs to stakeholders, and balancing engineering effort against business risk.
Data Integration and Flow Design
Design how systems exchange synchronize and manage data across a technology stack. Candidates should be able to map data flows from collection through activation, choose between unidirectional and bidirectional integrations, and select real time versus batch synchronization strategies. Coverage includes master data management and source of truth strategies, conflict resolution and reconciliation, integration patterns and technologies such as application programming interfaces webhooks native connectors and extract transform load processes, schema and field mapping, deduplication approaches, idempotency and retry strategies, and how to handle error modes. Operational topics include monitoring and observability for integrations, audit trails and logging for traceability, scaling and latency trade offs, and approaches to reduce integration complexity across multiple systems. Interview focus is on integration patterns connector trade offs data consistency and lineage and operational practices for reliable cross system data flow.
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.
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.
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.