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 Platform and Analytics
Assessment of approaches to modernize data infrastructure and build analytics capability that enables data driven decision making across the enterprise. Topics include data platform architecture and topology, choices between streaming and batch processing, data storage and modeling patterns, data ingestion and pipelines, extract transform and load strategies, metadata and cataloging, data quality and governance, security and privacy controls, self serve business intelligence and reporting, analytics tooling and operationalization of models, data engineering and analytics team organization, vendor versus build tradeoffs, migration strategies from legacy systems, measurement frameworks and key performance indicators, and how data capability investments accelerate and de risk broader transformation initiatives.
Data Engineering and Analytics Transformation
Building and evolving data engineering and analytics capabilities to enable data driven decisions at scale. Areas include selecting and operating data platforms designing event and batch architectures data modeling and semantic layers pipeline orchestration data quality and observability metadata and governance and enabling self service analytics. Candidates should explain how they prioritized data investments, drove adoption across teams, measured business impact, and linked analytics work to broader transformation goals.
Data and Analytics Infrastructure
Designing building and operating end to end data and analytics platforms that collect transform store and serve event product and revenue data for reporting analysis and decision making. Core areas include event instrumentation and tag management to capture user journeys marketing attribution and experimental events; data ingestion strategies and connectors; extract transform load pipelines and streaming processing; orchestration and workflow management; and choices between batch and real time architectures. Candidates must be able to design storage and serving layers including data warehouses data lakes lakehouse patterns and managed analytical databases and to choose storage formats partitioning and indexing strategies driven by volume velocity variety and access patterns. Data modeling for analytics covers raw event layers curated semantic layers dimensional modeling and metric definitions that support business intelligence and product analytics. Governance and reliability topics include data quality validation freshness monitoring lineage metadata and cataloging schema evolution master data considerations and role based access control. Operational concerns include scaling storage processing and query concurrency fault tolerance and resiliency monitoring and observability alerting cost and performance trade offs and capacity planning. Finally candidates should be able to evaluate and select tools and frameworks for orchestration stream processing and business intelligence integrate analytics platforms with downstream consumers and explain how architecture and operational choices support marketing product and business decisions while balancing tooling investment and team skills.
Data and Analytics for Transformation
Covers the end to end data and analytics considerations needed to measure and guide transformation initiatives. Topics include data architecture patterns such as data lakes and warehouses, event streaming and real time processing, batch pipelines, instrumentation strategies to capture adoption and impact signals, data pipeline design, analytics model selection, process mining techniques to discover current state and bottlenecks, visualization and dashboard design for executive and operational reporting, data quality and lineage, and tool selection and tradeoffs for analytics and reporting.