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.