Sr Principal Product Owner – Data Management & Integration Services

Keurig Dr Pepper•Frisco, TX

36d•$130,000 - $180,000

About The Position

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. 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.

Requirements

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

Nice To Haves

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)

Responsibilities

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.
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.
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.
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.
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 Governance Executive Board / Integration Governance sub-council to align platform, gateway, and AI capabilities with regulatory and business standards.