Job Overview:

Are you ready to redefine how data becomes enterprise intelligence - and to own the integration backbone that lets AI act on it?
At Keurig Dr Pepper, we're building the future of data - one domain, one signal, one intelligent decision at a time. Great AI doesn't start at the model; it starts at the data and the seams between systems. Our strategy is to converge these patterns into one governed Enterprise Integration Gateway - standardize, secure, govern - so that trusted, catalog-governed data can be both created and consumed by AI: agents, models, and copilots operating in the flow of work.
We're looking for a product owner who can own both ends: the modern data platform that makes data AI-ready, and the enterprise integration gateway that lets AI discover, govern, and act on that data. If you thrive at the intersection of AI engineering, enterprise integration, and data operations - and you want to turn bleeding-edge technology into real business transformation - this is your moment.
Your Mission
As the Sr Principal Product Owner - Data Management & Integration Services, you will execute KDP's Data Mgmt and AI Data Readiness strategy for next-generation data platforms, and drive the AI integration strategy that optimizes for AI data creation and consumption. You will own the capabilities that acquire, move, standardize, and govern enterprise data - from raw ingestion through the Enterprise Integration Gateway (API management, event streaming/CDC, microservices, iPaaS, and agent/MCP patterns) to domain-based data ownership, medallion-architected pipelines, and AI-embedded DataOps. This role is central to KDP's Unified Architecture and to making our data - structured and unstructured - trusted, discoverable, and ready for an AI-augmented enterprise.
What You'll Do
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 12 Silver 12 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
Governance & Compliance
• 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 Governance Executive Board / Integration Governance sub-council to align platform, gateway, and AI capabilities with regulatory and business standards
Who You Are
• A delivery-focused technologist with expertise across product management, enterprise integration, data operations, and AI enablement
• An AI-forward product owner who understands what it takes to make data AI-ready and to let agents, models, and copilots safely create and consume it
• A disciplined executor who can translate complex business needs into scalable, well-integrated, well-governed solutions
• An integration-minded leader who understands that trusted AI starts with reliable acquisition, movement, and governance of data across systems
• A collaborative leader who thrives in cross-functional environments and drives alignment across business, technical, and external partner stakeholders

Total Rewards:

• Salary range $130,000 - $180,000
• Actual placement within the compensation range may vary depending on experience, skills, and other factors
• Benefits, subject to election and eligibility: Medical, Dental, Vision, Disability, Paid Time Off (including paid parental leave, vacation, and sick time), 401k with company match, Tuition Reimbursement, and Mileage Reimbursement
• Annual bonus based on performance and eligibility

Requirements:

Must-Haves
• Bachelor's degree in Computer Science, Information Technology, Business Administration, or equivalent experience
• 7+ years in data engineering / data platform product management and/or enterprise integration (middleware, iPaaS, API platforms), with a focus on large-scale data platforms and/or integration at enterprise scale
• Demonstrated product ownership / backlog management - roadmap ownership, prioritization, and sprint delivery
• Proven experience in multi-tier environments across business, technology, and operations
• Expertise in Agile methodologies (Scrum/SAFe), user-centered design, and backlog management
Preferred / Differentiators
• AI data readiness & AI engineering enablement - making structured/unstructured data AI-ready; enabling ML/feature stores, RAG, or agentic/AI consumption; AI-augmented DataOps (a core focus of this role)
• AI integration strategy - designing how AI creates and consumes data through governed, discoverable interfaces (API/microservices, event/CDC, agent frameworks / MCP)
• Enterprise integration platforms & gateway - API gateways, middleware/ESB/iPaaS, or integration-hub/gateway management (e.g., MuleSoft, Informatica, TIBCO, Dell Boomi, webMethods)
• API gateway / microservices - API lifecycle, REST design, gateway architecture, event-driven patterns, API standards, and legacy-to-modern migration (priority capability)
• Modern data platform - Snowflake, Databricks (incl. AI enablement such as Genie / AgentBricks / Mosaic AI), medallion/lakehouse, Unity Catalog, Informatica, Alation/Collibra
• SAP integration - interfaces, IDocs, BAPIs, SAP PI/PO
• Secure file transfer - SFTP/MFT platform management, partner onboarding/migration, key management
• EDI / B2B - transaction sets, trading-partner management
• Regulated / security-compliance - experience (SOX, NIST, Zero-Trust, or similar), including AI governance awareness
• CPG / Manufacturing / Supply Chain - industry experience (beverage, food, or FMCG preferred)