Product Director - IT Data & Analytics

Novolex•Lake Forest, IL

4d•$150,000 - $263,000•Hybrid

About The Position

The Product Director, IT Data & Analytics is responsible for converting the strategic data roadmap into value-creating data products within the enterprise data and portfolio. The role is the primary interface between the business and the IT Data & Analytics organization: it translates operational, supply chain, commercial and financial demands into a manageable backlog of data products, works with the scrum master for coordination of sprints and advises data engineering, data architecture and reporting teams for optimal deliverables. This is not a hands-on development role, but it requires enough technical depth to technically understand the products they own and have detailed conversations about them with IT peers and business counterparts, discussing and negotiating features and improvements. It also advises and guides data architects and data engineers to evaluate proposed designs, and to ensure that solutions are scalable, reusable, well documented and supportable over time.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, Analytics, Business Administration or a related field.
Minimum 10 years of progressive experience in data and analytics, business intelligence or enterprise IT, including at least 5 years leading a team, product or portfolio.
Minimum 5 years of experience in a manufacturing, industrial or consumer products environment, with direct exposure to plant operations, supply chain and commercial processes.
Demonstrated experience as a Product Owner or Product Manager in an Agile environment (Scrum, Kanban or SAFe), including ownership of a prioritized backlog, definition of acceptance criteria and acceptance of delivered work.
Experience managing analytics delivery with a mix of internal, offshore and partner resources.
Experience working directly with senior business stakeholders at director and vice president level, including presenting to and influencing executive audiences.
Experience managing a portfolio or program budget and third-party vendors under contract.
Working knowledge of modern data platform architecture, including data warehouse and lakehouse patterns, layered architecture, ETL/ELT design, dimensional modeling and semantic models — sufficient to evaluate and challenge technical design decisions without performing the build.
Ability to read SQL and to interpret data models, source-to-target mappings and data lineage.
Familiarity with enterprise source systems commonly found in manufacturing: ERP (SAP or equivalent), manufacturing execution and shop-floor / OEE systems, supply chain planning, CRM, and financial consolidation or EPM platforms.
Working knowledge of business intelligence and visualization platforms (Power BI or equivalent), including semantic model and workspace governance.
Understanding of data governance, master data management, data quality and information security practices.
Strong written and verbal communication skills, with the ability to adjust between executive, functional and technical audiences.
Demonstrated ability to prioritize competing demands, make decisions with incomplete information, and influence outcomes in a matrixed organization.

Nice To Haves

Certified Scrum Product Owner (CSPO/PSPO) or SAFe Product Owner / Product Manager certification.
Experience in a multi-site, multi-business-unit organization, including acquisition integration and consolidation of ERP or reporting landscapes.
Experience with a modern cloud data platform such as Databricks, Snowflake, Microsoft Fabric or Azure Synapse.
Experience implementing or operating an application managed services (AMS) support model.
Experience implementing a data governance or data stewardship program.
Exposure to Lean, Six Sigma or other continuous improvement methodologies.

Responsibilities

Serve as the primary point of contact between Data & Analytics and business leadership across Operations, Manufacturing, Supply Chain, Commercial, Finance and Procurement, and across business units.
Develop and maintain a multi-year data and analytics backlog aligned to enterprise and business unit data objectives.
Lead intake, qualification and prioritization of business demand; balance competing priorities, sequence work against available capacity, and communicate trade-offs to sponsors.
Define business cases, expected benefits and success measures for analytics investments, and report on portfolio progress, adoption and value realization to senior leadership.
Control the portfolio budget across capital and operating expense; support annual planning and forecasting cycles.
Own the product vision, roadmap and prioritized backlog for the data and analytics portfolio.
Have a deep functional and technical understanding of the products they own, and proposes and negotiates features and releases that keep the product current and continuously adding value.
Author epics, features, user stories and acceptance criteria that clearly define business requirements and expected outcomes for delivery teams.
Lead backlog refinement, sprint planning, reviews and retrospectives in partnership with Scrum Masters and delivery leads; review and accept completed work against agreed acceptance criteria and definition of done.
Plan releases and coordinate user acceptance testing, go-live, hypercare and transition to ongoing support.
Drive adoption after deployment through training, communication and change management, including retirement of legacy and duplicative reporting.
Lead, coach and develop a team of Product Owners and Business Analysts; set priorities, performance expectations and development plans.
Advise data architects and data engineers on data modeling, data architecture and integration approaches, ensuring designs satisfy business requirements and align with enterprise standards.
Participate in design and architecture reviews, evaluating proposed solutions for scalability, reusability, performance, supportability and total cost of ownership.
Promote consistent practice in dimensional and semantic modeling, naming and documentation conventions, source-to-target mapping, data lineage and dataset certification, in partnership with Data Architecture and Data Governance.
Champion reuse of certified data products over single-purpose solutions, and identify duplication across business units and functions.
Ensure delivered solutions include the documentation, runbooks and knowledge transfer required for ongoing support.
Partner with Data Governance and Information Security teams on data quality, stewardship, access management and compliance requirements.
Manage relationships with systems integrators, managed service providers and software vendors supporting the portfolio.
Define scope, deliverables and acceptance criteria for statements of work; monitor partner performance, deliverables and knowledge transfer.
Evaluate build-versus-buy and internal-versus-partner delivery options and recommend the approach that best fits cost, capability and risk.