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.