Design and implement governance standards leveraging Databricks Unity Catalog. Define least-privilege access models, attribute-based access control policies, data classification, tagging, and lineage standards. Establish monitoring for system health, jobs, pipelines, audit activity, data quality, and platform performance. Implement platform approaches for cost monitoring and optimization.
Define data architecture standards for Bronze, Silver, and Gold medallion layers. Establish reusable patterns for source ingestion, transformation, data quality, lineage, and deployment. Architect configuration-driven onboarding frameworks using Databricks Lakeflow, Asset Bundles, YAML/control tables, and automated CI/CD. Guide development of conformed dimensions, shared business keys, and cross-domain data products.
Define the architecture for an AI-ready Gold layer with certified, governed, documented data products. Design semantic and context layers using governed business definitions, metric views, KPIs, metadata, and business glossary information. Architect natural-language analytics using Databricks Genie. Define agent-ready interfaces using governed Unity Catalog functions, MCP endpoints, tracing, and evaluation.
Provide architectural direction and technical mentorship to Data & AI Engineers. Partner with business SMEs and data owners to translate requirements into scalable technical solutions. Lead architecture and design reviews. Establish reusable engineering standards, reference architectures, runbooks, and implementation patterns. Participate in milestone and steering reviews.
Deliver a secure, observable, governed, cost-aware, AI-ready Databricks architecture that can scale beyond a single use case. Produce assessment scorecards, remediation plans, platform standards, reusable data architecture patterns, and semantic and AI architecture assets. Enable operational readiness with monitoring, runbooks, documentation, and knowledge transfer.
Experience Requirements
Deep understanding of Databricks Lakeflow connect capabilities and AWS analytics and ingestion services.
Extensive experience addressing ingestion challenges from unsupported sources by Lakeflow connect, including CDC handling and reconciliation techniques.
Expertise with data quality libraries and configuration-based frameworks that plug DQ into ingestion pipelines.
Extensive experience designing enterprise Databricks lakehouse architectures with Spark, Delta Lake, Lakeflow, and Unity Catalog.
Strong experience with medallion architecture and Bronze/Silver/Gold data patterns.
Experience designing enterprise data governance, lineage, security, and access-control frameworks including ABAC and RBAC, data classification, tagging, and least-privilege security.
Experience designing production-grade data pipelines, reusable ingestion frameworks, observability, CI/CD, deployment automation, and production support.
Experience designing semantic layers and governed enterprise data products, and architecting AI or GenAI solutions operating against governed enterprise data, including Databricks Genie and AI/BI.
Experience with MCP and agent-based architectures, GenAI governance, tracing, and evaluation preferred.
Strong communication skills and ability to work across engineering, security, architecture, and business teams.
