- Own the platform strategy, roadmap, and delivery across data onboarding,
access provisioning, and consumption capabilities, balancing business demand, engineering dependencies, and operational risk.
- Lead and develop global, distributed engineering teams delivering data
pipelines, APIs, front-end data products, and self-service analytics; establish technical standards, delivery accountability, and cross-team collaboration.
- Direct data onboarding and integration using Unity Catalog, Delta Sharing,
and Lakehouse Federation, enabling governed access across workspaces, catalogs, and lines of business.
- Partner with senior business, product, technology, audit, and compliance
stakeholders to prioritize investments, resolve competing objectives, and communicate architectural choices and delivery trade-offs.
- Establish and enforce onboarding controls for data classification, metadata,
lineage, and access, ensuring source-data classification approval and clear ownership before data is made available for consumption.
- Own platform production readiness, scalability, resilience, and audit
readiness; drive remediation of control gaps involving service identities, credential lifecycle, classification coverage, and schema changes.
- Define and monitor service-level objectives for stakeholder-facing data
products, with clear accountability for reliability, support, and operational performance. Evaluate and adopt Databricks-native capabilities, including declarative pipelines and conversational analytics, where they improve usability, governance, delivery efficiency, or cost.
- Set standards for enterprise-authorized AI-assisted and agentic engineering
workflows across teams, including automated testing and release governance, with appropriate human oversight, security, validation, resilience, and traceability.
- Sets and scales multi-department strategy for agentic AI-enabled engineering
and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration; establishes guardrails for validation, security, resiliency, traceability, and reuse.
- Applies knowledge of tools within the Software Development Life Cycle
toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments.