Sr Data Analytics Engineer HR IL - Bloomington; MN - Mankato; MN - Lakeville

Empowered to live. Inspired to work. Compeer Financial is a member‑owned cooperative located in Illinois, Minnesota and Wisconsin. We bring together team members with a variety of backgrounds and experiences to help provide financial services to support agriculture and rural communities. Join us in a culture that not only promotes meaningful work and professional development, but provides a flexible, hybrid work environment and excellent benefits, which empower you to thrive both personally and professionally.

• Hybrid model – up to 50% work from home
• Flexible schedules including ample flexibility in the summer months
• Up to 9% towards 401k (3% fixed Compeer contribution plus up to 6% match)
• Benefits: medical, dental, vision, HSA/FSA, life & AD&D insurance, short‑term and long‑term disability, wellness program & EAP
• Vacation, sick leave, holidays/floating holidays, parental leave, and volunteer paid time off
• Learning and development programs
• Cross‑functional committee opportunities (i.e. Inclusion Council, emerging professional groups, etc.)
• Professional membership/certification reimbursement and more!

Casual/seasonal & intern team members are not eligible for benefits except for state‑mandated programs.

This position offers a hybrid work option up to 50% remote and is based out of either Bloomington, IL;
Mankato, MN; or Lakeville, MN office locations.

This position is responsible for building analytics tools that utilize the data pipeline to provide actionable insights for decision making. The incumbent operationalizes and maintains machine learning models developed by data scientists/quantitative analysts within the organization to ensure that the machine learning models or data analytics work product developed by data scientists are efficiently transitioned into robust, scalable and maintainable production systems.

• Develops data models, data pipelines and streamlines the deployment of models into production environments that could include machine learning.
• Supports the design and implementation of Power BI (PBI) dashboards and semantic models, enabling intuitive visualization of advanced analytics
• Builds and maintains pipelines for data analytics projects that include machine learning, enabling seamless integration and delivery of model updates.
• Monitors the performance and accuracy of projects in production and performs regular maintenance to ensure they continue to meet business needs.
• Works closely with quantitative analysts to understand their models' requirements and provides the necessary infrastructure and tooling for model/data analysis training and experimentation.
• Optimizes performance of SQL queries, predictive models and data pipelines across both on‑prem and cloud environments for speed, efficiency and cost‑effectiveness.
• Designs, builds, deploys and maintains data integration pipelines in MS Azure and/or SQL Server Integration Service.
• Documents data engineering workflows, predictive analytics integration patterns, advanced Power BI development standards and MLOps processes to support transparency, reproducibility and efficient onboarding.

• Identifies, designs and implements internal process improvements, automates manual processes, optimizes data delivery, etc.
• Participates with cross‑functional Data and Business Technology teams to formulate best practices.
• Stays up‑to‑date with the latest analytics and predictive analytics tools, technologies and best practices to continuously improve the machine learning operations pipeline.

• Facilitates meetings with Data, Project Delivery and/or business unit team members.
• Provides information and training to other team members. Serves as a resource for questions and problem resolution.

• Bachelor’s degree in math, computer science, management information systems, or related field or an equivalent combination of education and experience sufficient to perform the essential functions of the job.
• Minimum of 7 years of experience with various MS SQL environments (2008, 2012, 2014, 2017,…