Director, Data & Analytics Product Management

Director, Data & Analytics Product Management

The Director of Data & Analytics Product Management is responsible for defining and executing the organization's enterprise data and analytics product strategy. This position leads a portfolio of data, analytics, reporting, and artificial intelligence products that expand the organization's ability to generate insights, share information, improve operational performance, and advance strategic objectives.

The Director establishes the product vision, roadmap, investment priorities, and governance practices for the organization's data and analytics product portfolio. Working closely with executive leaders and stakeholders across business functions, operations, finance, and technology, the Director translates organizational needs into scalable, secure, and user-centered data and analytic products and capabilities.

This position leads a multidisciplinary team responsible for product management, data product engineering, analytics delivery, and related capabilities. The Director oversees the entire product lifecycle, including discovery, business case development, prioritization, design, development, implementation, adoption, performance measurement, and continuous improvement. The role ensures that product investments are aligned with organizational strategy, technical architecture, data governance requirements, and measurable business outcomes.

The Director serves as a senior advisor to organizational leaders on data and analytic product opportunities, analytics capabilities, artificial intelligence, emerging technologies, and approaches for increasing the value of the organization's data assets. The position also represents the data and analytics product function in enterprise planning, vendor engagements, and external partnerships.

PRIMARY JOB RESPONSIBILITIES

Data and Analytics Product Strategy

Define, communicate, and execute the organization's enterprise data and analytics product strategy in alignment with organizational priorities, business objectives, and technology strategy.

Establish a clear product vision and multiyear roadmap for enterprise data, analytics, reporting, artificial intelligence, and information-sharing products.

Identify and evaluate opportunities for new data and analytic products, analytical services, AI-enabled capabilities, and reusable data assets that increase the value, accessibility, and responsible use of organizational data.

Translate organizational strategies and complex business challenges into clear product opportunities, investment recommendations, and actionable delivery plans.

Develop product business cases that articulate user needs, expected outcomes, resource requirements, dependencies, risks, and anticipated organizational value.

Product Portfolio Leadership and Governance

Lead the planning, prioritization, and management of a portfolio of enterprise data and analytics products, balancing near‑term requirements with long‑term strategic investments.

Establish and manage a transparent product intake, evaluation, prioritization, and roadmap‑planning process.

Define portfolio governance practices, decision rights, prioritization process, product standards, and performance expectations across the data and analytics product lifecycle.

Evaluate product performance and portfolio health using clearly defined objectives, key results, adoption measures, service‑level expectations, business outcomes, and return‑on‑investment indicators.

Lead quarterly and annual product planning processes, including resource allocation, dependency management, capacity planning, and roadmap adjustments.

Identify and proactively manage product risks, technical dependencies, data limitations, implementation barriers, and organizational change impacts.

Establish processes for retiring, consolidating, or modernizing products that no longer meet business, technical, or user requirements.

Product Development and Lifecycle Management

Oversee the full lifecycle of data and analytics products, from user research and discovery through design, development, launch, adoption, support, and continuous improvement.

Lead the development and delivery of scalable data products, curated datasets, analytics applications, dashboards, data pipelines, APIs, decision‑support tools, and AI‑enabled solutions.

Ensure product teams apply user‑centered product management practices, including stakeholder interviews, requirements definition, prototyping, usability testing, and structured feedback collection.

Translate business needs into clear product requirements, user stories, acceptance criteria, roadmaps, and measurable success indicators.

Establish product management standards and reusable practices for discovery, documentation, backlog management, release planning, testing, deployment, support, and product performance evaluation.

Partner with data architecture, engineering, analytics, security, and governance teams to ensure products are scalable, interoperable, reliable, secure, and compliant.

Ensure appropriate product documentation, metadata, data lineage, quality controls, operating procedures, and support models are established and maintained.

Drive continuous improvement of product development methods, technical platforms, delivery processes, and team operating models.

Artificial Intelligence and Emerging Capabilities

Develop and oversee the product strategy for artificial intelligence, generative AI, automation, advanced analytics, and machine learning capabilities.

Identify high‑value opportunities to embed AI and automation into existing products, improve organizational efficiency, strengthen decision‑making, and create new services.

Lead the evaluation, prototyping, testing, implementation, and monitoring of AI‑enabled products in partnership with technical, legal, privacy, security, compliance, and business stakeholders.

Evaluate emerging platforms, tools, and vendor capabilities to determine their potential business value, technical fit, scalability, and total cost of ownership.

Stakeholder Engagement and Product Adoption

Build trusted relationships with executives, business leaders, subject matter experts, technical teams, and external partners to understand strategic priorities and product needs.

Facilitate cross‑functional decision‑making and align stakeholders around product vision, scope, priorities, resource requirements, and success measures.

Develop and execute product adoption strategies, including communications, training, stakeholder engagement, change management, and ongoing user support.

Establish structured mechanisms for gathering user feedback, evaluating product satisfaction, and incorporating insights into product roadmaps.

Communicate product strategy, portfolio performance, delivery progress, risks, dependencies, and outcomes to executive and nontechnical audiences.

Serve as a trusted advisor to organizational leaders on data products, analytics opportunities, digital capabilities, artificial intelligence, and emerging technologies.

Team and Organizational Leadership

Recruit, lead, and develop a high‑performing team of data and analytic product managers, data and analytic engineers, analysts, and other data and analytic delivery professionals.

Establish clear roles, responsibilities, performance expectations, and development plans for team members.

Build product management capabilities across the team through coaching, mentoring, standardized practices, professional development, and communities of practice.

Create an inclusive, collaborative, and accountable team culture focused on customer needs, measurable outcomes, continuous learning, and delivery excellence.

Coordinate internal teams, contractors, and technology partners to ensure successful product delivery and operational support.

Vendor, Financial, and Operational Management

Lead the evaluation, selection, and management of vendors, consultants, software providers, and strategic technology partners supporting data and analytics products.

Develop requests for proposals, statements of work, vendor scorecards, service‑level expectations, and contract performance measures.

Manage vendor relationships to ensure delivery quality, cost effectiveness, security, compliance, knowledge transfer, and alignment with product roadmaps.

Provide oversight of the data and analytics product management budget, including forecasting, resource planning, software investments, vendor spending, and financial performance.

Monitor product operations, service performance, user support, data quality, and production issues, ensuring timely resolution and clear accountability.

Governance, Risk, and Compliance

Ensure that data and analytics products comply with applicable legal, regulatory, licensing, contractual, privacy, security, accessibility, governance, and ethical requirements.

Partner with data governance leaders to establish standards for data ownership, stewardship, access, quality, classification, retention, lineage, and responsible use.

Incorporate privacy, security, accessibility, data quality, and compliance requirements into product design and lifecycle management.

Establish appropriate controls for products using sensitive, confidential, proprietary, health‑related, or personally identifiable information.

JOB QUALIFICATIONS

Required

Bachelor's degree in product management, data science, computer science, information systems, informatics, business administration, public health, health care administration, or a related field.

At least 12 years of progressive experience in data and analytics, digital product management, technology product development lifecycle, business intelligence, data engineering, or related discipline.

At least 7 years of experience leading data, analytics, technology, or product management teams, including direct people‑management responsibility.

Demonstrated experience defining and executing enterprise data, analytics, or digital product strategy.

Demonstrated experience leading a portfolio of complex, cross‑functional products, programs, or platforms with competing priorities and interdependent delivery teams.

Experience developing product visions, roadmaps, business cases, user requirements, prioritization frameworks, release plans, and product performance measures.

Demonstrated ability to translate organizational strategy and business needs into scalable data and analytics products.

Experience establishing product management operating models, governance practices, intake processes, prioritization criteria, and lifecycle standards.

Strong understanding of data architecture, data modeling, data integration, metadata, data quality, data governance, analytics platforms, and data lifecycle management.

Experience overseeing the development of data products, curated datasets, dashboards, analytical applications, APIs, data pipelines, or AI‑enabled solutions.

Working knowledge of agile, product‑led, and iterative development methodologies, including backlog management, user stories, acceptance criteria, release planning, and retrospectives.

Experience managing budgets, vendors, consultants, contracts, and technology investments.

Experience defining and using product metrics, objectives and key results, adoption measures, customer satisfaction indicators, and business outcome measures.

Experience working with relational databases, SQL, business intelligence platforms, and modern data and analytics environments.

Strong understanding of privacy, security, compliance, accessibility, licensing, and ethical considerations related to data and AI products.

Demonstrated ability to build and retain high‑performing multidisciplinary teams.

Ability to operate effectively in a complex, matrixed, and fast‑paced environment while managing multiple strategic priorities.

Preferred

Master's degree in business administration, product management, data science, computer science, information systems, informatics, public health, health care administration, or a related field.

Formal product management training or certification, such as Pragmatic Institute, Product School, AIPMM, SAFe Product Management, Certified Scrum Product Owner, or a comparable program.

Experience working with medical or dental claims data, electronic health records, dental practice management systems, health care data standards, or other regulated health data.

Experience developing or managing external‑facing data products, data exchanges, customer portals, digital platforms, or subscription‑based data services.

Experience with cloud data and analytics platforms such as Microsoft Azure, Amazon Web Services, Databricks, Snowflake, or comparable technologies.

Experience with modern analytics and business intelligence platforms such as Power BI, Tableau, Sigma, or comparable tools.

Familiarity with data product concepts such as data mesh, domain‑oriented data ownership, data contracts, APIs, semantic layers, and reusable data services.

Experience with DevOps, DataOps, MLOps, CI/CD pipelines, automated testing, observability, and version‑control platforms such as Git.

Experience developing or managing artificial intelligence, generative AI, machine learning, natural language processing, or intelligent automation products.

Knowledge of prompt engineering, retrieval‑augmented generation, model evaluation, responsible AI, and monitoring of AI‑enabled products.

Familiarity with statistical and analytical tools such as Python, R, SAS, or Stata.

Experience with user research, service design, design thinking, customer journey mapping, usability testing, and product adoption strategies.

Experience working in a nonprofit, health care, public health, insurance, research, or mission‑driven organization.