Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Designs, builds, and governs enterprise ontologies, taxonomies, semantic models, knowledge graphs, metadata services, and semantic APIs. Partners with product, AI, analytics, platform, and business teams to enable RAG, agentic AI, contextual search, reasoning, interoperability, and analytics consistency. Establishes metadata governance, quality controls, stewardship, lifecycle management, and reusable engineering patterns. Evaluates semantic technologies, mentors engineers, and communicates technical trade-offs and recommendations to diverse stakeholders.

Leads enterprise strategy, architecture, governance, and implementation for semantic layers, knowledge graphs, ontologies, taxonomies, and AI-enabled knowledge capabilities. Partners with AI, data science, product, engineering, and business teams to enable trusted AI, analytics, search, interoperability, and reusable knowledge. Establishes metadata and knowledge lifecycle standards, advances RAG and semantic search innovation, and builds a high-performing team of semantic and knowledge engineers.

Owns a portfolio of NovaOS capabilities from concept through delivery, adoption, and continuous improvement. Responsibilities include managing product backlogs and roadmaps, prioritizing features, defining requirements and success metrics, coordinating cross-functional engineering, AI, UX, data, and architecture teams, leading product discovery and release planning, monitoring adoption and KPIs, and ensuring alignment with product governance, architecture, and Responsible AI standards.

Leads the strategy, roadmap, and product organization for NovaOS, an enterprise AI operating system. Oversees a portfolio spanning data, semantic, agentic, and experience layers; partners with business, engineering, and technology leaders to prioritize investments and deliver measurable outcomes. Establishes product governance, lifecycle practices, success metrics, and feedback mechanisms while building and mentoring a high-performing product management team.

Leads enterprise core data management by defining standards for data, metadata, modeling, and quality; driving governance adoption; and delivering trusted, AI-ready datasets. Oversees data operations, service levels, documentation, metrics, risk management, and continuous improvement. Partners with engineering, business teams, vendors, and senior stakeholders while leading and developing Associate Directors and distributed teams.

Leads strategic external data partnerships supporting customer engagement and integrated marketing. Oversees vendor performance, spend, data quality, security, compliance, governance, and integration into enterprise platforms. Establishes SLAs, manages data acquisition and stewardship, resolves quality issues, reports partnership performance to leadership, and coordinates training. Evaluates emerging data offerings, AI-enabled capabilities, and partnership models to improve marketing effectiveness, customer engagement, decision-making, and enterprise value.

Leads the engineering strategy, architecture, development, operations, and continuous improvement of an enterprise agentic AI foundation. Establishes scalable engineering standards, operating models, governance, and reusable platform services that enable AI products and intelligent workflows. Partners with product, data, infrastructure, security, and business teams to modernize existing systems, guide cross-functional delivery, monitor operations, and communicate priorities, risks, and investment needs to senior leaders. Manages and develops product or engineering leaders while driving measurable business value.

Leads demand intake, prioritization, governance, portfolio planning, resource capacity alignment, and delivery oversight across the Data Governance and Data Management Office. Develops executive dashboards, monitors portfolio health and milestones, escalates risks and dependencies, facilitates governance forums, standardizes program-management practices, and drives continuous improvement. Partners with senior leaders and cross-functional teams to align data initiatives with business value, organizational capacity, and strategic priorities.

Leads enterprise data product strategy, roadmaps, prioritization, adoption, governance, and lifecycle management for reference data, master data, metadata, and standards. Defines reusable, AI-ready products containing business meaning, rules, metrics, lineage, and context. Partners with data enablement, technology, analytics, governance, and business teams to scale reliable products, establish standards and service expectations, measure value, resolve issues, and drive stakeholder adoption.

Coordinates technical onboarding and ongoing operations for data partners and vendors. Ensures data delivery, ingestion, metadata, catalogs, documentation, and inventories remain accurate and current. Tracks data quality, SLAs, delivery performance, operational metrics, and issue resolution across business, technical, engineering, analytics, and vendor teams. Maintains centralized issue trackers, escalates risks, translates technical problems into business impacts, and drives cross-functional follow-through in a matrixed environment.