Senior Data Architecture Manager

A. Architecture Assessment & Strategic Roadmap

• Evaluate the current data engineering framework end-to-end, including 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-versus-impact trade-offs.

B. Data Estate Governance

• Build and maintain a comprehensive inventory of the data estate, cataloging all source systems (onboarded and prospective) and the subject areas each covers (both ingested and not yet ingested).

• Establish this inventory as a living artifact that informs onboarding decisions, coverage analysis, and platform planning.

C. Standards Definition & Enforcement

• Design, integrate, or refactor naming conventions for schemas, tables, views, orchestration jobs, and pipelines, including the migration approach for transitioning to new standards where needed.

• Define standardized ingestion and processing patterns across the full medallion architecture, including sub-layering strategy, format standardization (Parquet, Avro, Delta), secure PII ingestion, data normalization, technical data quality tracking, row- and column-level access controls, late-arriving dimension management, and data export workflows.

• Establish clear pattern selection criteria so engineers know which approach to apply for a given source type or use case.

• Define and operationalize the exception management process for handling justified deviations from established standards.

D. Hands-On Implementation

• Build production-grade boilerplate code for each standardized pattern using the existing GCP toolchain, including BigQuery, Cloud SQL, Cloud Composer, Dataflow, Dataproc, Cloud Storage, Pub/Sub, and related services.

• Ensure templates are modular, well-documented, and immediately adoptable by the engineering team.

E. CI/CD & Developer Experience

• Support the integration of data engineering pipelines with the CI/CD solution, aligning with the broader CI/CD modernization initiative’s timeline and tooling decisions.

• Contribute to developer experience improvements that reduce friction in pipeline development, testing, and deployment.

F. Knowledge Transfer & Enablement

• Author the Source Onboarding Playbook, a repeatable step-by-step guide for bringing new data sources into the platform, covering initial assessment, pattern selection, naming convention application, quality gates, access control setup, and production release.

• Mentor and upskill data engineers on the new standards, patterns, and tooling through documentation, walkthroughs, and hands-on pairing.