Senior Principal Architect, EDT Data Architecture Strategy & Standards

Engineering & Technical Leadership: - Personally design and build end-to-end batch and streaming solutions, including CDC ingestion, Auto Loader, Databricks Workflows/DLT, ADF orchestration, and PySpark/SQL medallion transformations, using realistic enterprise data and production-grade engineering practices. - Review production code and pipeline design at the pull-request level, reproduce performance issues, tune Spark jobs and compute configurations, assess DBU efficiency, and pair with engineers to implement and verify improvements.

Senior Principal Architect, EDT Data Architecture Strategy & Standards The Senior Architect, Data & Analytics Architecture owns the enterprise data and analytics architecture strategy, standards, and design patterns for the global Azure/Databricks Lakehouse ecosystem. Actively shapes architectural and technical decisions across programs through direct, hands-on engagement. This is a player-coach architect role: the incumbent is accountable both for defining the target-state architecture and for defending it in real design decisions, intervening with authority and technical credibility to guide teams to the right path. Standards published by this role are grounded in working reference implementations and current engineering practice, not theory. What you will do:

Architecture Strategy, Standards & Best Practices: - Define and own the enterprise data & analytics reference architecture and multi-year roadmap across the Azure Lakehouse estate, while staying directly engaged in solution design, prototyping, and technical validation across ADLS, Databricks, Delta Lake, Unity Catalog, Power BI, Databricks Genie, and Copilot-based AI experiences. - Build and maintain working reference implementations and reusable blueprints for lakehouse zoning, Delta table design, ingestion and orchestration, semantic models, and Unity Catalog security patterns; validate each standard through executable code, deployment scripts, and representative data workloads.

- Bachelor’s or master’s degree from an accredited university in the areas of Computer science, Engineering, Information Systems, Business, or equivalent field of study required. - 10+ years of progressive experience in data engineering, data architecture, and analytics across large, global enterprises, including recent hands-on delivery on Azure/Databricks Lakehouse platforms. - Demonstrated design and build depth: Ability to walk through data platforms and pipelines they have personally architected and implemented — not solely governance, advisory, or oversight experience.

- Demonstrated design and build depth: Ability to walk through data platforms and pipelines they have personally architected and implemented — not solely governance, advisory, or oversight experience. - Expert-level command of the core stack: Azure, ADLS, Databricks (Spark, Delta Lake, Unity Catalog, Workflows/DLT), modern data engineering practices, and Power BI, with good knowledge of Databricks Genie and Microsoft Copilot. - Proven experience defining and operationalizing enterprise architecture standards, reference architectures, and design authority or architecture review processes.

- Create and exercise engineering lifecycle standards for data quality, automated testing, CI/CD, observability, lineage, and recovery; run PoCs, architecture spikes, and hands-on solution health assessments before standards are adopted broadly. - Serve as the senior technical counterpart to Microsoft and Databricks by testing preview capabilities in sandbox environments, documenting findings, and converting vendor direction into practical reference patterns, guardrails, and implementation recommendations. What you need:

- Define and own the enterprise data & analytics reference architecture and multi-year roadmap across the Azure Lakehouse estate, while staying directly engaged in solution design, prototyping, and technical validation across ADLS, Databricks, Delta Lake, Unity Catalog, Power BI, Databricks Genie, and Copilot-based AI experiences. - Build and maintain working reference implementations and reusable blueprints for lakehouse zoning, Delta table design, ingestion and orchestration, semantic models, and Unity Catalog security patterns; validate each standard through executable code, deployment scripts, and representative data workloads. Design Authority & Decision Intervention:

- Personally design and build end-to-end batch and streaming solutions, including CDC ingestion, Auto Loader, Databricks Workflows/DLT, ADF orchestration, and PySpark/SQL medallion transformations, using realistic enterprise data and production-grade engineering practices. - Review production code and pipeline design at the pull-request level, reproduce performance issues, tune Spark jobs and compute configurations, assess DBU efficiency, and pair with engineers to implement and verify improvements. - Create and exercise engineering lifecycle standards for data quality, automated testing, CI/CD, observability, lineage, and recovery; run PoCs, architecture spikes, and hands-on solution health assessments before standards are adopted broadly.