Data Cloud Integration Product Manager

Job Title:

Data Cloud Integration Product Manager

Location:

East Hanover, NJ Hybrid (3 days (Mon-Thurs), 2 remote)

Pay rate: ***/hour - ***/hour (Lowest would be better)

*** is on a mission to transform medicine and improve lives worldwide. As a global leader in healthcare, we leverage advanced technology and data to deliver patient-centric solutions, enhance customer engagement, and drive innovation. We collaborate closely with the US business, bringing insights and challenging ideas to empower smarter, data-driven decision-making. The US CRM organization sits within Strategy, Platforms & Transformation (SPT) - AI & Platform Products and plays a crucial role in driving the transformation to a next-generation Customer
360 operating model.

*** seeks an accomplished product leader with a track record of turning business demand from multiple commercial functions into a well-managed data product backlog. Strong prioritization judgment, stakeholder partnership, and hands‑on data fluency are essential to success in this role.

Reporting to Director, PO Audience Activation & Marketing Intelligence, Data Cloud Integration Product Manager owns the enterprise integration capability that brings governed, reusable, and reliable data into Data Cloud and makes shared data products available across Data Spaces. The role converts requests for new Data Streams and enterprise analytics views into prioritized pipeline work, defines reusable integration patterns, coordinates architecture and delivery dependencies, and ensures that Data Streams, Data Lake Objects, batch data transforms, calculated insights, and cross-space reporting models are built and maintained to enterprise standards.

• Manage integration intake:
  Receive and qualify requests for new Data Streams, source-system connections, shared data products, enterprise analytics views, and cross-space reporting needs.
• Shape enterprise priorities:
  Prioritize enterprise-level data pipeline development based on business value, reuse potential, platform dependencies, data readiness, capacity, and operational risk.
• Define integration scope:
  Translate source-to-target needs into clear epics, features, interface requirements, data contracts, mapping needs, acceptance criteria, and delivery plans.
• Own shared integration backlog:
  Maintain and refine the backlog for enterprise Data Streams, Data Lake Objects, batch data transforms, identity and harmonization dependencies, calculated insights, and reporting models.
• Plan pipeline delivery:
  Scope pipeline work and cross-space reporting models during backlog planning; prepare Data Stream, Data Lake Object, and Enterprise Analytics Space mappings for PI planning.
• Establish reusable patterns:
  Partner with architects and the IT Center of Excellence to define common ingestion, transformation, error-handling, observability, testing, and deployment patterns.
• Coordinate cross-space dependencies:
  Align enterprise Data Cloud work with Data Space Product Owners, source-system owners, governance, architecture, analytics, and scrum teams; surface sequencing and capacity trade-offs.
• Lead build and lifecycle management:
  Guide the development and ongoing maintenance of Data Streams, Data Lake Objects, batch data transforms, and cross-space calculated insights from design through production support.
• Assure data quality and reliability:
  Define and validate data quality, reconciliation, lineage, refresh, monitoring, and operational support expectations for shared integrations.
• Measure and communicate performance:
  Track pipeline delivery health, reliability, reuse, data quality, and adoption; communicate risks, decisions, and outcomes to platform leadership and stakeholders.

KPI area What good looks like

Delivery predictability Committed integration scope is delivered with transparent dependencies, risks, and release readiness.

Pipeline reliability Shared data pipelines meet agreed refresh, stability, monitoring, and support expectations.

Data quality Source-to-target data is reconciled and meets defined completeness, validity, and usability criteria.

Reuse and standardization Enterprise patterns and shared data products reduce duplicative integration work across Data Spaces.

Backlog health Integration work is prioritized, clearly specified, testable, and ready for sprint and PI planning.

Operational excellence Incidents, defects, technical debt, lineage, and ownership are visible and actively managed.

• Bachelor's…