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).
Metric Definition and Implementation
Defining and computing business metrics correctly: single-source-of-truth metric definitions, handling edge cases (dedup, attribution windows, timezones), and reconciling real-time vs batch metric values. Covers metric governance and translating business questions into precise, reproducible calculations. A high-frequency analytics-interview topic.
Experimentation Platforms and Infrastructure
Infrastructure for A/B testing and experimentation: assignment/bucketing, metric pipelines for experiments, guardrail and variance-reduction plumbing, and experiment result storage. Covers building the platform that powers trustworthy online experiments at scale. Distinct from the statistics of experiment analysis.