Director, AI, Data and Developer Enablement

Meijer•Grand Rapids, MI

1d•Hybrid

About The Position

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

Requirements

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

Nice To Haves

Master's degree preferred.

Responsibilities

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.
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.
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.
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.