Data Engineer

The role – what you’ll do

Barr is seeking a Data Engineer to join ourMinneapolis, Minnesota, team. In this hybridrole, you will support the Information Systems group, playing a key part in designing and maintaining reliable data pipelines, integrating with third-party systems and APIs, and contributing to the evolution of our ETL architecture across a variety of use cases.You’llcollaborate with cross-functional teams, including data scientists and machine learning engineers, to align data strategies with AI objectives.

A successful person in this role is curiousand proactive about exploring new data technologies that support innovation in AI and analytics. They enjoy partnering with cross-functional teams and using their expertise to help define practical strategies that improve data quality, maintainability, and reliability while supporting the organization’s growing needs around AI readiness and emerging technologies. This person also enjoys collaborationand brings a thoughtfulapproach to data engineering.

Your impact – key responsibilities

• ETL development and maintenance: Troubleshoot, maintain, and monitor ETL processes built with SQL Server Integration Services (SSIS), Python, or other processes.Review existing ETL patterns to identify areas for optimization, makerecommendations, and contribute to the redesign of integration workflows.

• Data observability and monitoring: Improve traceability by building audit and logging capabilities into ETL pipelines.Partner with teams to strengthen observability by improving alerting, error handling, and operational dashboards that keep ETL health visible and actionable.

• Integration architecture:Architect and support robust API and point-to-point integrations with internal and third-party systems—bridging data silos and unlocking smoother automation across software tools.

• DevOps and change management:Contribute and manage structured change management and version control practices using Azure DevOps to support consistent development and deployment of ETL workflows.

• Data engineering standards and best practices: Help set the tone for data engineering excellence by guiding best practices in pipeline structure, metadata usage, and data quality—helping ensure each workflow is built to scale and evolve.

About the opportunity

• Compensation: Anticipated range of $98,000–115,000 annually. Compensation will vary based on relevant experience, education, skill level, and other compensable factors. Employees in this position may also be eligible for a discretionary cash bonus based on team and individual performance. This position is classified as exempt under the Fair Labor Standards Act.

• A hybrid work arrangement may be considered for this position. A hybrid work arrangement refers to splitting time worked between a Barr office and a home office. This position is based out of Barr's Minneapolis, Minnesota, office.

About you – required core competencies 

• Education:Bachelor’s degree in computer science, data engineering, information systems, or a related field or equivalent experience.

• Experience:Five or more years of relevantexperience in data engineering or ETL development, building and managing data pipelines in a production environment.

• Knowledge of SQL relational databases (especially SQL Server), including writing complex SQL queries and change data capture ETL practices.

• Experience developing and maintaining ETL processes using SQL Server Integration Services (SSIS).

• Experience defining or contributing to data governance frameworks, including data quality, access management, and metadata practices.

• Software/programs:SQL Server Integration Services (SSIS), Microsoft Azure data services (i.e., Azure SQL Database, Azure Data Factory, or Azure Storage); proficiency in a scripting or programing language (e.g., Python, .Net, or PowerShell).

• Communication: Strong interpersonal, oral, and written communication skills and the ability to communicate effectively with a variety of different people.

• Must be legally authorized to work in the United States without the need for sponsorship by Barr, now or in the future.

Helpful additional experience (not required)

• Familiarity with NoSQL databases such as MongoDB, Redis, or DynamoDB.

• Exposure to vector databases (e.g., Pinecone, Weaviate, Qdrant, Milvus).

• Awareness of data patterns in AI/ML systems, including prompt logging, model output storage, and managing metadata associated with generative AI workflows.

• Basic knowledge of graph databases (e.g., Neo4j, Amazon Neptune).

#LI-Hybrid