Data Cloud Integration Product Manager

HireLifeScience; East Hanover (NJ); Hybrid; USD 69,000 - 117,000; Full time

Job summary

HireLifeScience is seeking a Data Cloud Integration Product Manager in East Hanover, NJ. This hybrid role blends on-site and remote work to drive data-enabled decision making across commercial functions.

You will own enterprise data integration capabilities, translate requests into backlog priorities, and coordinate with architects, data engineers, and product teams to deliver scalable, measurable data products.

Qualifications

• Bachelor's degree in data, technology, business, engineering, computer science, or related field.
• Strong fluency in English; other languages are desirable.
• 5+ years of product management, data integration, data engineering, platform delivery, or technical product ownership experience.

Responsibilities

• Receive and qualify requests for new Data Streams and shared data products.
• Prioritize enterprise-level data pipeline development based on business value and reuse potential.
• Define scope with epics, features, interfaces, and data contracts.
• Maintain backlog for Data Streams, Data Lake Objects, and analytics space patterns.
• Coordinate cross-space dependencies and surface capacity trade-offs.
• Guide development from design to production support and ensure data quality.

Skills

Data integration

Agile backlog

Stakeholder mgmt

Data quality

Cross-functional leadership

Education

Bachelor's degree

Advanced degree preferred

Tools

Salesforce Data Cloud

Snowflake

DevOps / CI-CD

CRM integration

Job description

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

• Bachelor's 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.