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 Pipeline Architecture and Design
End-to-end design of data pipelines: source-to-sink flow, staging layers, idempotency, backfills, and reprocessing. Covers choosing between batch and streaming stages, decoupling ingestion from transformation, and designing for evolvability. The foundational systems-design skill for a data engineering interview.
Data Quality and Validation
Ensuring correctness and trust in data: validation rules, constraints, completeness/accuracy/timeliness checks, and quality frameworks. Covers designing validation into pipelines, quality gates before publishing, and handling edge cases and real-world dirty data. Central to any data engineering or analytics role.
Business Intelligence, Reporting, and Dashboards
The reporting and presentation layer of analytics: semantic/metrics layers, report development and automation, self-service BI, and the architecture that feeds dashboards and reports. Covers dashboard and visualization design (tool selection across Tableau/Power BI/Looker-style platforms, drill-downs, information architecture, communicating metrics visually), refresh strategies, and query performance for interactive reporting workloads. Spans both the engineering behind the reporting layer and the design of the dashboards that consume it.
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.