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.