Director, AI, Data and Developer Enablement

Data Engineering, Analytics & AI/Automation 

• Lead the design, development, and implementation of data engineering, analytics, and AI/automation solutions to support business objectives. 

• Oversee data architecture, ensuring data integrity, security, and scalability. 

• Manage and mentor a team of data engineers, data scientists, and analysts, fostering a culture of collaboration and continuous improvement. 

• Collaborate with cross-functional teams to identify data needs and develop strategies to leverage data for business insights and decision-making. 

• Drive adoption of best practices in data management, analytics, and AI/automation. 

• Ensure compliance with data governance policies and regulations. 

• Stay current with industry trends and emerging technologies in data engineering, analytics, and AI/automation. 

• Develop and manage budgets, resources, and timelines for data projects. 

• Ensure all teams follow engineering and IT standards for change controls and IT practices for production systems. 

Enterprise Quality Adoption 

• Own the enterprise quality strategy — embed quality into the software development lifecycle, not onto it. 

• Drive adoption of test automation, shift-left testing, and continuous quality practices across all engineering teams. 

• Define and enforce quality standards, frameworks, and tooling across the portfolio; ensure consistent adoption at scale. 

• Partner with engineering and product teams to establish quality gates that protect production stability without slowing delivery. 

• Report on quality health across domains, with clear visibility into defect rates, test coverage, and release readiness. 

Engineering Delivery Performance — DORA Metrics 

• Establish DORA metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, Mean Time to Recovery) as the standard measurement framework for engineering delivery health. 

• Own the baseline, targets, and reporting cadence for DORA metrics across teams; surface trends to senior leadership with clear business context. 

• Use DORA data to identify delivery bottlenecks, prioritize platform and process investments, and demonstrate improvement over time. 

• Connect engineering performance to business outcomes — faster delivery and lower failure rates translate directly to customer experience and cost efficiency at Meijer's scale. 

• Partner with DevOps and platform teams to build the tooling and observability infrastructure required to measure and improve DORA outcomes. 

IT General Controls (ITGC) 

• Accountable for ITGC compliance across the technology domains in scope — change management, access controls, computer operations, and program development controls. 

• Partner with Internal Audit, Compliance, and Finance to ensure controls are designed, operating effectively, and audit-ready. 

• Own remediation of ITGC deficiencies; drive root cause analysis and sustainable control improvements rather than point-in-time fixes. 

• Ensure all teams understand and operate within ITGC requirements as a standard part of the delivery process — not a compliance afterthought. 

• Maintain documentation, evidence, and control narratives sufficient to support SOX and internal audit cycles. 

• Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field. Master's degree preferred. 

Experience 

• 10+ years of experience in data engineering, analytics, and AI/automation, with at least 5 years in a leadership role. 

• Proven experience establishing and scaling enterprise quality practices across large engineering organizations. 

• Hands-on experience implementing DORA metrics programs and using delivery performance data to drive engineering improvement. 

• Demonstrated experience with ITGC compliance, SOX controls, or equivalent control frameworks in an enterprise environment. 

• Track record of managing multiple complex programs simultaneously in a fast-paced, high-scale environment. 

Technical Skills 

• Strong knowledge of data architecture, data warehousing, ETL processes, and data modeling. 

• Proficiency in Python, Java, or Scala; experience with big data technologies including Spark, Kafka, and Databricks. 

• Expertise in machine learning and AI frameworks (TensorFlow, PyTorch, scikit-learn or equivalent). 

• Familiarity with CI/CD tooling, test automation frameworks, and observability platforms used to track delivery and quality metrics. 

• Working knowledge of ITGC control domains: logical access, change management, computer operations, and program development. 

Leadership & Communication 

• Strong communication and interpersonal skills; able to collaborate with and influence stakeholders at all levels. 

• Speaks the language of business outcomes — connects technology performance to cost, revenue, and customer experience. 

• Proven ability to manage multiple priorities and drive accountability across matrixed teams.