Sr Principal Product Owner – Data Management & Integration Services

AI Integration Strategy — One Front Door for AI (Emphasis Area) Drive the AI integration strategy that optimizes for AI data creation (AI-ready ingestion, enrichment, and pipeline generation) and AI data consumption (agents, models, apps, and copilots acting on trusted data). Own the roadmap for the Enterprise Integration Gateway — converging a fragmented estate of ETL, B2B/EDI, API/app, and file-transfer integrations into one governed, composable, enterprise-wide gateway that standardizes, secures, and governs how data and services cross every boundary. Establish modern integration patterns as the enterprise standard: API management, event streaming / CDC, microservices, iPaaS, and agent frameworks / MCP — enabling AI agents (autonomous and assisted, human-in-the-loop), models (reasoning over catalog-governed data), and copilots (in the flow of work via discoverable APIs). Champion an API-first, microservices strategy: API lifecycle management, REST API design, gateway architecture, event-driven patterns, and API standards (auth, versioning, rate limits, error contracts, observability, deprecation), leading the migration from legacy point-to-point patterns to reusable integration services. AI Data Readiness & AI Engineering Enablement Deliver capabilities that make data AI-ready — unifying structured and unstructured data, and enabling reasoning over trusted, catalog-governed assets Execute AI-embedded, AI-augmented DataOps — automated governance, anomaly detection, and intelligent metadata discovery — treating AI as a force multiplier for pipeline creation, data management, and self-service (e.g., Databricks AI enablement such as Genie, AgentBricks, and Mosaic AI) Partner with platform/AI engineering to enable ML/feature workloads and AI consumption — Unity Catalog-governed models and feature stores, cataloged prompts and datasets for auditability, and discoverable APIs that put trusted data in the flow of work Execution of Strategic Data Capabilities Deliver platform capabilities that support raw data ingestion, profiling, and domain-based ownership across the enterprise Operationalize medallion architecture (Bronze → Silver → Gold) to support scalable, governed data pipelines fed by enterprise integration flows Apply the enterprise decision framework (federate to prove value, migrate to optimize and govern) to balance speed-to-value with compute/storage efficiency Translate business needs into prioritized backlogs and sprint plans that accelerate AI enablement and data readiness Domain Stewardship & Marketplace Partnership Enable domain stewards to manage and activate their data assets through platform capabilities and tooling Partner with the Enterprise Data Marketplace team to ensure seamless integration, lineage, and discoverability of curated, AI-ready data products Stakeholder Engagement Collaborate with Enterprise AI Services, business units, data stewards, integration/trading partners, and technical teams to align on governance, access policies, connectivity, and AI-consumption patterns Facilitate cross-functional collaboration across business, technology, operations, and external vendor/partner teams to deliver high-value data products and dependable integrations Ensure robust metadata management, lineage tracking, and policy enforcement across all data domains — including data-in-motion across every integration boundary (preventing governance gaps at the seams) Establish data contracts at each system boundary (schema, SLA, classification, owner, glossary references) and an approved connector/pattern catalog that scales self-service without sacrificing consistency Apply security hardening and compliance discipline (SOX, key management for file transfer, partner security, Zero-Trust access) and align with AI governance for responsible, auditable AI (model inventory, GenAI/RAG and agentic-AI controls) Support next-generation strategies with Day-1 readiness and go-live integration support Collaborate with the Data...