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)
Job Purpose
*** 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 Customer360 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.
Major Accountabilities
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.
Key Performance Indicators
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.
Ideal Background
Bachelors degree in data technology business engineering computer science or a related field required; advanced degree preferred.
Fluent English; other languages are desirable.
5 years of product management data integration data engineering platform delivery or technical product ownership experience.
Strong knowledge of data integration concepts APIs batch and streaming pipelines transformation data contracts schema mapping lineage observability and data quality.
Hands-on understanding of Salesforce Data Cloud including Data Streams Data Lake Objects data modeling identity resolution dependencies calculated insights and activation workflows.
Demonstrated experience leading an agile backlog and coordinating delivery across architects engineers analysts QA source-system owners and business product teams.
Strong ability to translate business and analytics needs into scalable technical requirements and explain architecture trade-offs to nontechnical stakeholders.
Preferred
Experience with Salesforce Snowflake or comparable cloud data platforms Veeva CRM integration and enterprise analytics ecosystems.
Familiarity with CI/CD DevOps metadata management data cataloging and production support practices.
Background in pharma life sciences or another regulated data-intensive industry.
Leadership Competencies
Navigate complexity
Enable impactful and timely decision-making across business data technology privacy and compliance stakeholders.
Identify the critical issues in complex situations maintain focus on enterprise outcomes and adapt priorities as conditions change.
Take a long-term view of platform sustainability downstream impacts and reusable enterprise capabilities.
Deliver collective impact
Integrate diverse perspectives to achieve the best outcome for the enterprise.
Influence without authority and collaborate effectively across organizational boundaries.
Challenge assumptions constructively and make decisions grounded in evidence.