Director Data & Analytics
$154k - $231kMedline Industries, LP
Job Summary The Director, Enterprise Data Products & Decision Intelligence Center of Excellence (COE) will define and lead Medline's enterprise approach to building, governing and scaling reusable data products and decision intelligence capabilities. This role provides strategic and operational leadership for the COE, establishing the standards, frameworks, operating model and talent needed to turn enterprise data into trusted, certified products that power analytics, AI and decision-making at scale. Partnering closely with business leaders across Supply Chain, Manufacturing, Finance, Sales, Quality, Procurement and other functions, the Director will shape how data is sourced, modeled, productized and activated moving the organization from descriptive reporting toward predictive, prescriptive and AI-assisted decisions. Success in this role requires deep techno-functional fluency, the ability to operate in ambiguity, strong product management discipline, and the leadership to balance immediate business needs with long-term enterprise data and AI innovation. Job Description MAJOR RESPONSIBILITIES: Leadership and Strategic Planning
- Define and own the multi-year vision, strategy and roadmap for enterprise data products and decision intelligence, aligned to enterprise and business unit objectives.
- Establish and lead the Data Products & Decision Intelligence COE, including its charter, operating model, engagement model, funding approach and success metrics.
- Lead intake, qualification and prioritization of enterprise demand; sequence work against capacity and communicate trade-offs to executive sponsors.
- Define business cases, expected benefits and value measures for data product and analytics investments, and report on portfolio progress, adoption and value realization to senior leadership.
- Own the enterprise data product portfolio, including product vision, roadmaps, prioritized backlogs and lifecycle management from ideation through deployment and retirement.
- Build and scale enterprise data foundations-reusable, governed and certified data products, conformed dimensions and semantic models-that serve multiple business functions.
- Establish data product standards, including ownership, SLAs, data contracts, documentation, quality thresholds and certification criteria.
- Champion reuse of certified data products over single-purpose solutions; identify and eliminate duplication across business units and functions.
- Partner with Data Governance and Information Security on data quality, stewardship, metadata, lineage, access management and compliance requirements.
- Lead the evolution from descriptive reporting to diagnostic, predictive and prescriptive analytics that embed insights directly into business decisions and workflows.
- Identify and deliver high-value decision intelligence use cases (e.g., demand sensing, inventory optimization, pricing, margin and cost-to-serve analytics, quality and supplier risk) in partnership with business and data science teams.
- Enable AI-ready data, including feature-ready datasets, semantic layers and governed data for machine learning, generative AI and agentic AI solutions.
- Define frameworks for measuring decision outcomes and closing the loop between insights, actions and business results.
- Establish COE standards, frameworks, reference architectures, templates and best practices for data products, semantic models, BI and analytics delivery.
- Promote data literacy and self-service analytics through training, communities of practice, workshops and certification programs.
- Drive adoption through change management and communication, including retirement of legacy and duplicative reporting.
- Lead governance of Service Integrators and delivery partners, ensuring delivery quality, cost effectiveness and alignment with architecture standards.
- Personnel Strategy: Lead recruiting, retention, performance management and career development for a team of data product managers, BI leaders, data architects and analysts.
- Talent Development: Mentor and coach leaders and team members, fostering a collaborative, high-performance and innovation-driven culture.
- Resource Allocation: Efficiently allocate internal and partner resources, ensuring timely delivery and minimizing business disruption.
- Policy and Procedure: Review, challenge and enhance departmental policies, standards and methodologies.
- Bachelor's Degree in Computer Science, Information Systems, Data Science, Business Administration, or a related field, or equivalent experience. Master's degree preferred.
- 10+ years of experience in data engineering, business intelligence, analytics or data management, with 4+ years in leadership roles managing data and analytics teams.
- Proven experience building or leading a Center of Excellence or similar enterprise capability.
- Demonstrated experience owning and scaling enterprise data products or data platforms in large, complex organizations.
- Strong background managing Service Integrators or large-scale delivery partners.
- Deep understanding of data product management principles, including product thinking, data contracts, lifecycle management and value measurement.
- Working knowledge of modern data platform architecture, including data warehouse and lakehouse patterns, medallion/layered architecture, ETL/ELT design, dimensional modeling and semantic models-sufficient to evaluate and challenge technical design decisions.
- Understanding of decision intelligence, advanced analytics, machine learning and generative AI concepts and how they apply to business decision-making.
- Working knowledge of business intelligence and visualization platforms (Power BI or equivalent), including semantic model and workspace governance.
- Strong understanding of enterprise data in the context of business goals, with the ability to connect data capabilities to measurable outcomes.
- Strong written and verbal communication skills, with the ability to adjust between executive, functional and technical audiences.
- Experience leading change management and adoption efforts in large organizations.
- Hands-on familiarity with Microsoft Fabric (OneLake, Lakehouse, Data Engineering, Real-Time Intelligence, Power BI) and Azure data services (Azure Data Factory, Synapse, ADLS, Azure Databricks, Azure Machine Learning).
- Experience with data governance and catalog tools (e.g., Microsoft Purview, Collibra, Alation).
- Experience leading modernization from legacy on-premise platforms to cloud data platforms.
- Domain experience in healthcare, medical products, manufacturing, distribution or supply chain.
- Experience with process, organizational and architectural constructs like data mesh, AI integration, ROI metrics.
- Certifications in Azure, Microsoft Fabric, data management (CDMP), product management or change management.
Vacancy posted 1 day ago
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