Director, AI, Data and Developer Enablement

Meijer
Meijer

Software Engineering, Data Science

Grand Rapids, MI, USA

Posted on Jul 12, 2026

As a family company, we serve people and communities. When you work at Meijer, you’re provided with career and community opportunities centered around leadership, personal growth and development. Consider joining our family – take care of your career and your community!

Meijer Rewards

  • Weekly pay

  • Scheduling flexibility

  • Paid parental leave

  • Paid education assistance

  • Team member discount

  • Development programs for advancement and career growth

Please review the job profile below and apply today!

Position will follow our hybrid schedule: Monday-Wednesday in Grand Rapids MI Corporate office, Thursday-Friday remote.


What You'll be Doing:

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.

What You Bring with You (Qualifications):

Education

  • 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.

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