Director of Business Strategy & Data Analytics

Position Summary

The Director of Business Strategy & Data Analytics is a strategic, cross-functional leader responsible for connecting enterprise data capabilities with the company’s business strategy and long-term value creation. In this dual-capacity role, the director partners closely with executive leadership, commercial teams, operations, supply chain, product line management, finance, and IT to translate business objectives into data-driven strategies that accelerate growth, strengthen operational performance, and enhance customer and supplier experiences.

This role sets the vision for enterprise master data governance, advanced analytics, and data-enabled transformation. The director ensures data is a trusted asset and leverages insights, predictive analytics, and business intelligence to influence strategic planning, guide investment decisions, and support the company’s competitive positioning within the industrial manufacturing sector.

Essential Duties and Responsibilities

Enterprise Data Governance & Master Data Strategy

• Define and lead the master data governance operating model for key enterprise domains (customer, product, supplier, material, asset).
• Partner directly with business process owners to ensure data accuracy, consistency, and reliability across commercial, supply chain, finance, and manufacturing operations.
• Oversee data quality programs, stewardship networks, and policies that improve global data standards, reduce operational friction, and support digital transformation initiatives.

Analytics, Insights & Decision Enablement

• Lead teams that deliver actionable insights, predictive analytics, dashboards, and modeling solutions that support strategic and operational decision-making.
• Collaborate with business leaders to define analytic use cases—such as demand forecasting, inventory optimization, pricing analytics, customer segmentation, procurement efficiency, and operational performance management.
• Drive adoption of self-service analytics across business teams and cultivate a culture of data fluency and fact-based decision-making.

Data Architecture, Operations & Technology Enablement

• Oversee the enterprise data environment, including data integration, data pipelines, MDM hubs, data warehouse/lake platforms, and analytics tools.
• Ensure the organization’s data architecture is scalable, secure, and aligned with current and future business needs, including Industry 4.0, IoT/OT data integration, and AI/ML initiatives in manufacturing operations.
• Evaluate and implement leading tools and platforms (e.g., SAP MDG, Informatica, Snowflake, Databricks, Teradata, Power BI, AI/ML frameworks) to strengthen enterprise insight capabilities and operational efficiency.

Compliance, Risk & Data Stewardship

• Ensure compliance with regulatory, privacy, and security requirements (GDPR, CCPA, etc.).
• Develop governance controls that reduce business risk, maintain audit readiness, and protect critical operational and customer data.

Innovation & Future-Ready Capabilities

• Monitor emerging trends in manufacturing analytics, AI/ML, supply chain digitization, and data governance.
• Lead proof-of-value experiments and pilot programs focused on improving manufacturing efficiency, product quality, and commercial performance through data-driven innovation.

Supervisory Responsibilities

Business & Strategy Leadership

• Serve as a strategic advisor to business unit leaders and the executive team, aligning data strategy with the organization's long-term business plan and operational priorities.
• Identify and champion high-impact opportunities where data, analytics, and digital capabilities can drive revenue growth, customer value, cost optimization, and operational resilience in a manufacturing environment.
• Lead the development of enterprise data and analytics roadmaps that support strategic initiatives such as, supply chain visibility, product lifecycle management, commercial excellence, and manufacturing performance.
• Establish KPIs and performance frameworks to measure progress on data maturity, strategic initiatives, and business value realization.

Cross-Functional Partnership & Change Leadership

• Serve as the primary liaison between business teams and IT for enterprise data and analytics initiatives.
• Build strong relationships with commercial, operational, manufacturing, supply chain, and financial leaders to shape and prioritize the data and analytics portfolio.
• Collaborate with Executive Leadership, IT, & functional leadership to ensure alignment across initiatives.
• Lead change management efforts to drive adoption of new processes, capabilities, and insights—ensuring solutions are practical, business-aligned, and value-focused.
• Develop and mentor a high-performing team across analytics, data engineering, master data, and governance.

Requirements and Preferred Skills

Education

• Bachelor’s degree in Business, Information Systems, Data Science, or related field required.
• Master’s degree preferred.

Experience

• 7–10+ years of progressive experience in data analytics, business strategy, digital transformation.
• Leadership experience guiding enterprise business intelligence or analytics teams. Driving master data programs preferred.
• Experience working in an industrial manufacturing or similar operationally complex environment strongly preferred.

Leadership & Soft Skills

• Exceptional executive communication, relationship building, and strategic influence skills.
• Ability to translate technical concepts into business language and value narratives.
• Strong analytical mindset with proven business acumen.
• Effective project leadership, prioritization, change management, and stakeholder alignment abilities.

Technical Skills

• Strong understanding of data governance principles and enterprise business intelligence strategy. MDM frameworks preferred.
• Experience with BI and analytics tools such as Power BI, advanced analytics, and/or machine learning environments.
• Proficiency with data integration, ETL/ELT, metadata management, and modern data platforms (Snowflake, Databricks, Teradata, etc.) preferred.