Manager, Data Engineering

Casey's•Ankeny, IA

4d•$125,100 - $174,100•Onsite

About The Position

Join Casey's Data & Analytics team as the Manager, Data Engineering! This role leads the Marketplace Data Engineering team delivering the data products that enable insights, analytics, data science, and AI across Casey's. Working closely with business and technology partners, you will lead a team of data engineers focused on building scalable, reliable, and reusable data solutions that drive business outcomes. As the Manager, Data Engineering, you'll be responsible for the strategy, delivery, and operational excellence of enterprise datasets and data products. Success in this role requires strong technical and people leadership, deep partnership with product and business stakeholders, and a passion for turning data into business value.

Requirements

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.
Demonstrated ability to translate business requirements into scalable technical solutions and drive execution through delivery.

Nice To Haves

Experience with modern cloud data platforms such as Databricks, Azure Data Lake Storage, Azure Data Factory, Microsoft Fabric, Snowflake, or similar technologies.
Experience developing and optimizing large-scale ETL/ELT data pipelines using SQL, Python, Spark, or similar technologies.
Knowledge of business intelligence platforms such as Power BI, Tableau, or Looker.
Experience supporting machine learning, AI, and advanced analytics initiatives through curated and governed data assets.
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 that enable business intelligence, analytics, data science, and AI initiatives. Responsible for hiring, coaching, performance management, career development, and succession planning of direct reports.
Create strong partnerships with business stakeholders, Product Owners, Data Scientists, and Analysts to define priorities and deliver high-value data solutions aligned to business outcomes. Partner closely with Product to co-own the data product roadmap, ensuring Engineering and Product operate as a unified, outcome focused delivery team.
Drive the design and implementation of scalable data pipelines, data models, and reusable data products.
Establish and promote data engineering best practices related to data quality, performance, security, and operational excellence.
Leverage AI-assisted engineering practices and emerging technologies to accelerate delivery, improve solution quality, and enhance team productivity.
Ensure data products are reliable, trusted, and well-documented through appropriate testing, monitoring, governance, and support processes.
Lead efforts to optimize data platform performance, scalability, reliability, and cost efficiency.
Define team goals and roadmaps that align with organizational priorities while ensuring work is appropriately prioritized across business and technology stakeholders.
Champion data product thinking by treating datasets as strategic assets with defined ownership and service levels.
Own the strategy, governance, reliability, and evolution of enterprise data products including customer, store, and operational 360 datasets (Guest, Store, Product) that serve as foundational assets for technology, analytics, AI, and business teams.