Evaluate the current data engineering framework end-to-end: medallion architecture layering, naming conventions, ingestion patterns, processing logic, security controls, and data quality mechanisms.
Benchmark the current state against industry best practices and produce a prioritized improvement roadmap with clear effort-vs-impact trade-offs.
Build and maintain a comprehensive inventory of the data estate — cataloging all source systems (onboarded and prospective) and the subject areas each covers (ingested and not yet ingested).
Establish this inventory as a living artifact that informs onboarding decisions, coverage analysis, and platform planning.
Standards Definition & Enforcement
Design, integrate, or refactor naming conventions for schemas, tables, views, orchestration jobs, and pipelines — along with the migration approach for transitioning to new stand...
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