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).
Customer Data Platforms and Marketing Data Integration
Unifying customer data across systems: CDP and CRM integration, identity resolution, segmentation and personalization at scale, and event-driven marketing data flows. Covers stitching first-party data into a single customer view and activating it downstream. The martech/customer-data slice of analytics infrastructure.
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.
Data Governance, Contracts, and Classification
Governing data at scale: data contracts between producers and consumers, schema evolution/compatibility, data classification and sensitivity tagging, access control, and lineage/cataloging. Covers policy, ownership, and compliance-driven controls over data. The governance layer over the technical stack.
Batch, Streaming, and Real-Time Serving Trade-offs
Reasoning about when to use batch, micro-batch, or continuous streaming and how to serve low-latency analytics: latency, cost, complexity, and correctness trade-offs; lambda vs kappa architectures; and reprocessing semantics. Covers real-time aggregation, freshness vs consistency trade-offs, and reconciling streaming results with batch ground truth, including geospatial and high-throughput real-time workloads under eventual consistency. The data-systems judgment topic for choosing and reconciling batch versus real-time approaches, distinct from the hands-on streaming transport itself.
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.
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.
Product Analytics Instrumentation and Event Tracking
Instrumenting products to collect behavioral data: event taxonomy/tracking plans, client and server-side collection, attribution implementation, and telemetry for web, mobile, and games (including crash reporting). Covers designing clean, analyzable event schemas and the collection infrastructure behind them. The data-collection foundation for product analytics.