Director of Data and AI

Wheeler Machinery•Salt Lake City, UT

23h

About The Position

The Director, AI & Data leads the Campbell Companies Center of Excellence (CoE) for data, AI, automation, and software engineering. The role is accountable for the enterprise data platform, data engineering, data governance, AI and agentic AI governance, automation, software development, and data and AI enablement across Campbell Companies and Wheeler Machinery. The Director builds and leads the operating model, organization, and platform that turn trusted data into governed AI and measurable business outcomes. This foundational leadership role is critical to Campbell Companies’ digital transformation strategy. The Director operates as a peer to the Director of Information Technology, collaborating on shared infrastructure, security, and end-user delivery while maintaining ownership of the data, AI, and automation domain. The Director defines the AI and Data strategy, multi-year roadmap, operating model, CoE organization, enterprise standards, architecture review, decision rights, KPIs, and value-realization reporting. The position owns the Microsoft Fabric enterprise data platform and its architecture, capacity, performance, cost management, integrations, and operations. It leads data engineering, platform operations, data governance, security and privacy, AI governance and risk management, AI solution delivery, agentic AI architecture and harness engineering, enterprise automation, data and AI enablement, and software engineering oversight. The role also manages operating and capital budgets, strategic vendors and platform relationships, and recruitment, development, and retention of a multidisciplinary team. It presents the AI & Data roadmap, governance posture, and results to the VP of Digital quarterly.

Requirements

Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related technical field.
At least 10 years of progressive technology leadership with depth in data engineering, AI/ML, or software development.
At least 5 years leading multidisciplinary teams and managing budgets.
Demonstrated experience building and operating data platforms at scale, such as Microsoft Fabric, Databricks, or Snowflake, using a medallion or equivalent layered architecture.
Proven track record deploying and governing AI solutions in a production enterprise environment, including LLM/RAG solutions.
Deep familiarity with Microsoft Fabric, including Lakehouse, Warehouse, Data Factory, Real-Time Intelligence, and Direct Lake; OneLake, Power BI, and Azure AI Services / Microsoft Foundry.
Strong working knowledge of data engineering practices, including SQL, Python, Spark, orchestration, testing, observability, and DataOps.
Understanding of software development lifecycles, CI/CD, DevSecOps, infrastructure as code, and agile delivery.
Knowledge of cloud security and identity, such as Microsoft Entra ID, role-based access, and Azure cost management.
Knowledge of data governance, cataloging, lineage, and master data management, such as Microsoft Purview.
Knowledge of AI governance frameworks, such as NIST AI RMF and ISO/IEC 42001, and applicable data privacy and AI regulations.
Ability to translate risk and compliance requirements into practical, enforceable standards.
Understanding of agent architectures, orchestration and tool-use patterns, Model Context Protocol, retrieval and vector search, and evaluation and tracing approaches for LLM systems.
Familiarity with agent harness concepts, including guardrails, permissions, human-in-the-loop controls, and monitoring autonomous workflows.
Familiarity with workflow automation and RPA platforms, such as Power Automate, and integrating automation with AI agents.
Experience designing and scaling a center of excellence or shared-services organization, including operating models, intake processes, and service levels.
Experience building enablement and adoption programs and leading organizational change.
Ability to attract, develop, and retain data and AI talent and build a succession-ready team.
Strategic thinking and ability to communicate complex technical concepts to executives and link investments to business outcomes.
Strong collaboration and influencing skills, including experience operating in a peer-leadership model.
Sound judgment balancing speed and innovation against risk, security, and cost.
Vendor management and negotiation skills.

Nice To Haves

Master’s degree.
Hands-on experience with agentic AI.
Experience in equipment dealership, heavy equipment, manufacturing, distribution, or another asset-intensive industry.
Microsoft Certified: Fabric Data Engineer Associate, Fabric Analytics Engineer Associate, Azure AI Engineer Associate, or Azure Data Engineer Associate.
Governance or leadership credentials such as CDMP, IAPP AIGP, ISO/IEC 42001 Lead Implementer, or PMP.

Responsibilities

Define and execute the AI and Data strategy, multi-year roadmap, and operating model aligned to business priorities and value targets.
Design and evolve the CoE organization across data engineering, data platform, AI and agentic solutions, automation, software engineering, and data enablement, establishing ownership, intake, prioritization, and service-level expectations.
Establish and enforce enterprise standards, architecture review, decision rights, and guardrails for business-unit and vendor-led work.
Define KPIs and value-realization reporting for adoption, cost, quality, risk, and business outcomes; present the roadmap, governance posture, and results to the VP of Digital quarterly.
Own the enterprise Microsoft Fabric data platform, including medallion Lakehouse architecture, OneLake governance, Fabric Data Factory, Real-Time Intelligence, and Power BI semantic models.
Set standards for data ingestion, transformation, modeling, and serving, including reusable patterns, data contracts, and domain-aligned data products.
Govern OneLake naming conventions, workspace design, partitioning, shortcuts, and access patterns; manage Fabric capacity, performance, cost, capacity planning, workload isolation, and chargeback or showback.
Integrate ERP, finance, service, parts, telematics, and equipment source systems into a consistent, governed analytical foundation.
Lead data engineering practices, including pipeline development, orchestration, testing, version control, and CI/CD for Fabric and Azure data assets.
Operate the data platform as a product with SLAs, monitoring, observability, incident response, and root-cause management.
Embed data quality, lineage, catalog management, and automated validation into pipeline operations at each medallion layer.
Drive platform engineering and infrastructure-as-code standards in partnership with the Director of IT.
Own the data governance framework, including data ownership and stewardship, business glossary, certified datasets, master and reference data, retention, and classification.
Implement role-based, row-level, and column-level security and sensitivity labeling in partnership with Information Security.
Ensure compliance with applicable data privacy and AI regulations, customer and OEM/partner data agreements, and internal audit requirements.
Establish and enforce AI governance, including a use-case registry, risk classification, approved tool and model standards, and sunset criteria; align governance to recognized standards and chair the AI governance or review board.
Define AI model evaluation, testing, bias and safety review, human oversight, auditability, and incident-response requirements before and after production release.
Ensure AI solutions receive appropriate security review, data-access controls, and change management before and after deployment.
Govern evaluation, prototyping, and production deployment of AI solutions using Claude Enterprise (Claude Code), Microsoft Copilot Studio, Microsoft Foundry, Azure AI Services, and LLM/RAG frameworks.
Manage the AI portfolio from intake through value measurement using build-versus-buy-versus-partner discipline, and retire solutions that do not deliver.
Define the enterprise reference architecture for agentic AI, including agent design patterns, orchestration, tool and API integration, Model Context Protocol, memory, and retrieval.
Own the agent harness, including shared runtime guardrails, evaluation and regression testing, tracing and observability, prompt and configuration management, cost and rate controls, and human-in-the-loop approval gates.
Establish identity, least-privilege access, audit logging, registry, and lifecycle management for agents as governed non-human identities.
Set autonomy tiers for agent actions and approvals, and monitor agent behavior in production.
Lead enterprise automation strategy across workflow automation, robotic process automation, API integration, and agent-driven processes.
Maintain an automation pipeline with intake, prioritization, ROI tracking, and support standards; coordinate with business process owners to redesign processes before automating them.
Lead data and AI enablement, including self-service analytics, certified datasets, reusable templates, and a champions network across business units.
Deliver training, data and AI literacy programs, responsible-use guidance, and a community of practice; measure adoption and business impact and lead change management for new capabilities.
Lead the Engineering & AI Solutions team across custom software development, legacy modernization, DevOps, and AI solution delivery.
Champion agile delivery, API design standards, secure development (DevSecOps), and CI/CD pipeline governance; set standards for AI-assisted development tools and code review.
Partner with the Director of IT on shared Azure tenant governance and DevOps standards.
Develop and manage AI & Data operating and capital budgets, including cloud consumption, licensing, and capacity forecasting.
Manage strategic vendors, consulting partners, and platform relationships, including contract terms, security and data-use requirements, and performance.
Recruit, develop, and retain data engineers, platform engineers, software engineers, automation developers, and AI specialists, with defined career paths and succession coverage.
Foster a culture of rigor, experimentation within guardrails, documentation, and continual learning.
Perform all other duties as assigned.