Data Engineering Manager — AI & Platform Leader

Casey's

Ankeny (IA)

On-site

USD 125,100 - 174,100

Full time

14 days+

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Benefits offered by this job

401k matching

Paid time off

Onsite cafeteria

Volunteer time

Onsite Child Development Center

Job summary

Casey’s is seeking a Manager, Data Engineering to drive the strategy, delivery, and reliability of enterprise data products across the organization. You will lead a team of data engineers delivering data products for analytics, AI, and business insights.

This role requires strong technical leadership, collaboration with product and business partners, and a passion for turning data into business value. The position focuses on enterprise datasets including Guest, Store, and Product data assets.

Qualifications

Bachelor’s degree in Computer Science, Information Technology, Data Analytics, Engineering, Mathematics, or related field, or equivalent work experience.
Seven (7)+ years of progressive experience developing and delivering enterprise data, analytics, or engineering solutions.
Three (3)+ years of experience leading and developing technical teams.
Experience managing teams responsible for data engineering, analytics engineering, business intelligence, or enterprise data platforms.
Strong understanding of data architecture, data modeling, data integration, and modern data engineering practices.
Experience building and supporting cloud-based data platforms and enterprise-scale data solutions.
Strong leadership, communication, organizational, analytical, and problem-solving skills.
Familiarity with data observability, metadata management, and data catalog solutions.

Responsibilities

Lead a high-performing team of Data Engineers responsible for building, maintaining, and supporting enterprise data products and datasets.
Define priorities with stakeholders and deliver high-value data solutions aligned to business outcomes.
Partner with Product to co-own the data product roadmap and ensure delivery as a unified team.
Drive the design and implementation of scalable data pipelines, data models, and reusable data products.
Promote data engineering best practices for data quality, performance, security, and governance.
Leverage AI-assisted engineering practices to accelerate delivery and improve quality.
Ensure data products are reliable, well-documented, and monitored.
Lead efforts to optimize data platform performance, scalability, and cost efficiency.
Set team goals and roadmaps aligned with organizational priorities.
Champion data product thinking with defined ownership and SLAs.