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).
Analytics Platforms and Dashboards
Comprehensive knowledge of analytics platforms, implementation of tracking, reporting infrastructure, and dashboard design to support marketing, product, and content decisions. Candidates should be able to describe tool selection and configuration for platforms such as Google Analytics Four, Adobe Analytics, Mixpanel, Amplitude, Tableau, and Looker, including the trade offs between vendor solutions, native platform analytics, and custom instrumentation. Core implementation topics include defining measurement plans and event schemas, event instrumentation across web and mobile, tagging strategy and data layer design, Urchin Tracking Module parameter handling and cross domain attribution, conversion measurement, and attribution model design. Analysis and reporting topics include funnel analysis, cohort analysis, retention and segmentation, key performance indicator definition, scheduled reporting and automated reporting pipelines, alerting for data anomalies, and translating raw metrics into stakeholder ready dashboards and narrative visualizations. Integration and governance topics include data quality checks and validation, data governance and ownership, exporting and integrating analytics with data warehouses and business intelligence pipelines, and monitoring instrumentation coverage and regression. The scope also covers channel specific analytics such as search engine optimization tools, social media native analytics, and email marketing metrics including delivery rates, open rates, and click through rates. For junior candidates, demonstration of fluency with one or two tools and basic measurement concepts is sufficient; for senior candidates, expect discussion of architecture, pipeline automation, governance, cross functional collaboration, and how analytics drive experiments and business decisions.
Experimentation Platforms and Infrastructure
Addresses the technical and organizational infrastructure required to run experiments at scale. Topics include randomization and assignment strategies, traffic allocation, instrumentation and metric collection pipelines, experiment configuration and rollout systems, experiment tracking and metadata, data quality and monitoring, guardrails to detect interference or contamination, automated validity checks, self service experimentation tooling, governance and permissions, and approaches to scale experimentation across many teams while preserving statistical validity. Senior conversations include designing experiment platforms, enabling self service and observability, and trade offs when scaling experiment velocity across products.
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.