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Director, Enterprise Data Management & Analytics Platforms

Job in Orrville, Wayne County, Ohio, 44667, USA
Listing for: The J.M. Smucker Co.
Full Time position
Listed on 2026-02-16
Job specializations:
  • IT/Tech
    Data Engineer, Data Science Manager
Salary/Wage Range or Industry Benchmark: 125000 - 150000 USD Yearly USD 125000.00 150000.00 YEAR
Job Description & How to Apply Below

Your Opportunity as the Director, Enterprise Data Management & Analytics Platforms

The Director, Enterprise Data Management & Analytics Platforms is responsible for the strategy, delivery, and ongoing operation of the company’s enterprise data, analytics, and AI platforms. This role serves as the single point of accountability for data platform capabilities across the enterprise, enabling scalable, trusted, and secure analytics in support of business growth.

Operating within a highly matrixed, publicly traded environment, this leader partners across IT and multiple business functions to deliver platform‑first data capabilities, while ensuring alignment with enterprise technology standards, controls, and financial governance.

This role leads a multi‑disciplinary organization of approximately 20 professionals and plays a critical role in positioning the company for AI‑driven decision‑making at scale.

Location: Orrville, OH (Close proximity to Cleveland/Akron)

Work Arrangements: Hybrid - onsite a minimum of 9 days a month primarily during core weeks as determined by the Company; maybe more as business need requires

In This Role You Will Enterprise Data & Analytics Platform Strategy
  • Own the enterprise data and analytics platform vision, strategy, and roadmap ensuring alignment to corporate priorities.
  • Establish and manage an insights delivery operating model to ensure adherence to enterprise standards while allowing for domain-specific analytics needs.
  • Drive ROI‑based prioritization and value realization across platform investments.
  • Define and promote practices that prioritize reusable, scalable, and governed data and analytics capabilities over point solutions.
  • Evolve the technical capabilities for the data and analytics platform in alignment with key initiatives and applicable corporate policies.
  • Establish enterprise standards for cloud data platforms, data integration, analytics tooling, and AI/ML enablement.
  • Partner with Enterprise Architecture and Infrastructure teams to ensure technology alignment, resilience, cost transparency, and scalability.
  • Manage vendor contracts, relationships and investment process.
People Leadership & Organization Management
  • Lead and develop a team including managers and senior technical leaders.
  • Build organizational capability through talent development, coaching, and succession planning.
  • Establish performance management, capacity planning, and operating rhythms aligned to enterprise expectations.
  • Manage change with Senior IT and Business Leadership.
Data Engineering & Platform Operations
  • Lead all enterprise data engineering functions, including ingestion frameworks, transformation patterns, scheduling, and orchestration.
  • Own platform administration and reliability, ensuring uptime, performance, cost efficiency, and operational maturity.
  • Implement Dev Ops and Data Ops best practices for deployment, monitoring, and incident management.
  • Ensure platforms meet SOX, audit, security, and regulatory requirements expected of a public company.
Data Architecture, Modeling & Integration
  • Define and govern enterprise data architectures to maintain overall consistency.
  • Accountable for the development of data models and semantic layers to support reporting, analytics, and self‑service.
  • Drive integration across ERP, CRM, Supply Chain, Finance, Consumer, and syndicated data sources.
  • Balance local business needs with enterprise‑level scale and reuse in a matrixed organization.
Data Enablement, Quality & Trust
  • Own the enterprise data enablement strategy and operating model, including governance, stewardship, metadata management, lineage, and quality.
  • Maintain authority and accountability for core data assets and analytic assets used for decision making.
  • Partner with Legal, Security, Privacy, and Compliance teams to ensure proper data usage, retention, and protection.
  • Implement automated data quality and observability practices to improve trust and adoption.
MLOps, AI & Advanced Analytics Enablement
  • Establish enterprise MLOps capabilities to support model development, deployment, monitoring, and governance.
  • Enable Data Science and Analytics teams with standardized, scalable AI and advanced analytics platforms.
  • Partner…
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