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).
Telemetry and Game Analytics
Instrumentation and analysis of gameplay and system telemetry to inform product and engineering decisions. Topics include event schema and naming conventions, sampling strategies and cost considerations for data collection, ingestion and storage pipeline design, metric definitions for retention and engagement, conversion funnels and cohort analysis, controlled experiments and hypothesis testing, dashboarding and visualization, data quality and governance, privacy and compliance concerns, and using analytics to prioritize bugs and drive iterative design. Candidates should be able to describe how they instrumented features, validated metric definitions, and used data to influence product and balance decisions.
Mobile Analytics and Crash Reporting
Design and operation of analytics and crash reporting for mobile products at scale. Topics include event instrumentation and session metrics, performance monitoring, crash capture and stack trace symbolication and deobfuscation, sampling and aggregation strategies to control data volume, reliable ingestion pipelines, experiment and metric design for A B testing, dashboards and alerting, privacy and consent handling, and techniques for reproducing and debugging field issues using telemetry.