Sr Principal Product Owner Data Management & Integration Services

Keurig Dr Pepper; Frisco (TX); On-site; USD 130,000 - 180,000; Full time

Benefits offered by this job

Medical

Dental

Vision

Disability insurance

PTO

401k matching

Tuition Reimbursement

Mileage reimbursement

Job summary

Keurig Dr Pepper seeks an AI Integration Strategy leader to shape the data and integration momentum across the enterprise. You will own the AI data readiness program, a unified gateway strategy, and a modern API/microservices baseline to securely move data across domains.

You will collaborate with platform, AI engineering, and business units to deliver scalable, governable data products, ensuring trusted data for AI consumption and decision making.

Qualifications

• Bachelor's degree in a related field and 7+ years in data engineering, data platforms, or enterprise integration.
• Proven backlog ownership, roadmap leadership, and sprint delivery in large-scale environments.
• Experience with API gateways, data governance, and secure data movement across systems.
• Strong expertise in Agile, SAFe, user-centered design, and cross-functional collaboration.

Responsibilities

• Drive the AI integration strategy for data creation, enrichment, and pipeline generation.
• Own the Enterprise Integration Gateway roadmap, consolidating ETL, API, and file transfers into a governed gateway.
• Champion API-first, microservices patterns; lead migration from legacy patterns to reusable integration services.
• Partner with platform and AI engineering to enable ML workloads, model catalogs, and auditable data flows.

Skills

AI integration strategy

Enterprise integration

API gateways

DataOps

Agile methodologies

Education

Bachelor's degree

Tools

MuleSoft

Informatica

TIBCO

Dell Boomi

webMethods

Snowflake

Databricks

Job description

AI Integration Strategy — One Front Door for AI

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

Candidate Profile

• Delivery‑focused technologist with expertise across product management, enterprise integration, data operations, and AI enablement.
• 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.
• Disciplined executor who can translate complex business needs into scalable, well‑integrated, well‑governed solutions.
• Integration‑minded leader who understands that trusted AI starts with reliable acquisition, movement, and governance of data across systems.
• 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). Bonus: Annual bonus based on performance and eligibility.

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
• Mileage Reimbursement

Required Education & Experience

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.

Key Skills & Expertise

• AI data readiness & AI engineering enablement — making structured/unstructured data AI‑ready; enabling ML/feature stores, RAG, or agentic/AI consumption; AI‑augmented DataOps.
• 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.
• 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).