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 and Data Quality
Designing, operating, and optimizing reliable data pipelines and ensuring data quality across ingestion, transformation, and consumption. Covers extract transform load and extract load transform patterns, efficient incremental and batch loading, idempotent processing, change data capture, orchestration and scheduling, and performance tuning to meet service level objectives. Includes data validation strategies such as schema enforcement, null and type checks, range and referential integrity checks, deduplication, handling late arriving and out of order data, reconciliation processes, and data profiling and remediation. Emphasizes observability, monitoring, alerting, and root cause analysis for data quality incidents, as well as data lineage tracking, metadata management, clear ownership and process discipline, testing and deployment practices, and governance to maintain data integrity for analytics and business operations. Also covers data integration concerns across customer relationship management systems, marketing automation systems, reporting systems, and other operational systems, including pipeline error handling, data contracts, and how test and validation checks can be integrated into pipelines to prevent regressions.
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.