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Data Engineer Ml Jobs in Minnesota (NOW HIRING)

AI Engineer - AI/ML

Minnetonka, MN · On-site

$116K - $140K/yr

Integrate data warehousing solutions like Snowflake and manage large-scale data pipelines ... ML techniques like Prompt Engineering, RAG (Retrieval Augmented Generation) and Agentic AI * 5+ ...

AI Engineer - AI/ML

Minnetonka, MN · Hybrid

$116K - $140K/yr

Integrate data warehousing solutions like Snowflake and manage large-scale data pipelines ... ML techniques like Prompt Engineering, RAG (Retrieval Augmented Generation) and Agentic AI * 5 ...

Work you'll do As an AI Engineer Consultant on the HC Forward team, you will design, build, and run the trusted, governed data + feature + retrieval layer used by AI/ML and GenAI solutions. You will ...

AI/ML Engineer

Minnetonka, MN · Remote

$98K - $176K/yr

Position Summary As an AI/ML Engineer , you will join our innovative technology team at Optum ... Implement and integrate semantic search capabilities to enhance data accessibility and retrieval ...

Python & SQL Strong Python programming for AI/ML and backend development, combined with SQL for querying, transforming, and analyzing enterprise data. * AI/ML Engineering & MLOps Experience ...

Data Strategy-Manager

Minneapolis, MN · On-site

$99K - $232K/yr

... Engineer / Data Analyst / ML - Proven leadership in data-driven strategies - Experience in defining data governance frameworks - Understanding of modern cloud data architectures - Knowledge of data ...

AI/ML Engineer Duration: 3-month contract (Could be extended for 6 months before conversion ... domain-specific data into AI systems 4. Agentic AI / AI Agents Experience building AI agents ...

Significant experience in AI/ML engineering with a strong data science background. * Demonstrated ability in designing and training LLMs. * Expertise in Databricks/Apache Spark for large-scale data ...

Significant experience in AI/ML engineering with a strong data science background. * Demonstrated ability in designing and training LLMs. * Expertise in Databricks/Apache Spark for large-scale data ...

Data Governance- Manager

Minneapolis, MN · On-site

$99K - $232K/yr

... Engineer / Data Analyst / ML Travel Requirements Up to 80% Job Posting End Date The salary range for this position is: $99,000 - $232,000. Actual compensation within the range will be dependent upon ...

You will partner with Data Engineering, Data Science, Architecture, Infrastructure, Security, and ... Provide technical leadership for AI/ML platforms including Palantir, AWS Bedrock, Amazon SageMaker ...

... Data Engineer/Data Analyst/ML - Proven leadership in data-driven strategies - Demonstrating thought leadership in data governance - Collaborating on strategy and transformation projects ...

Showing results 21-40

Data Engineer Ml information

What does a data engineer ML do?

A Data Engineer ML (Machine Learning) is responsible for designing, building, and maintaining the data pipelines and infrastructure necessary for machine learning applications. They clean, process, and organize large datasets to ensure data quality and accessibility for data scientists and ML engineers. In addition, they may work on deploying machine learning models to production environments and optimizing data workflows for efficiency and scalability.

What are the key skills and qualifications needed to thrive as a data engineer ML?

To thrive as a Data Engineer ML, you need strong programming skills (especially in Python or Scala), knowledge of data modeling, and a solid foundation in database technologies, typically supported by a degree in computer science or a related field. Familiarity with big data frameworks (like Spark or Hadoop), cloud platforms (AWS, GCP, or Azure), and ETL tools, as well as relevant certifications, is highly beneficial. Excellent problem-solving abilities, teamwork, and clear communication help you collaborate with data scientists and stakeholders effectively. These skills are essential for building robust data pipelines and infrastructure that enable scalable, high-quality machine learning solutions.

How do data engineer ML roles typically collaborate with data scientists and machine learning engineers on projects?

Data Engineer ML professionals work closely with data scientists and machine learning engineers by building and maintaining robust data pipelines, ensuring clean and reliable datasets are readily available for modeling and analysis. They often participate in meetings to understand model requirements, help optimize data storage for performance, and support the deployment of machine learning models into production environments. Effective collaboration involves continuous communication to troubleshoot data issues, implement data validation, and scale solutions as project needs evolve. This teamwork ensures that data-driven projects move efficiently from experimentation to deployment.

What is the difference between Data Engineer Ml vs Data Scientist?

AspectData Engineer MlData Scientist
Required CredentialsBachelor's in CS, Data Engineering certificationsBachelor's/Master's in CS, Data Science certifications
Work EnvironmentBuilding data pipelines, managing databasesAnalyzing data, creating models
Employer & Industry UsageTech companies, finance, healthcareResearch institutions, tech firms, finance

Data Engineer Ml focuses on developing and maintaining data infrastructure and pipelines, while Data Scientists analyze data and build predictive models. Both roles often collaborate but serve different functions within data teams.

What cities in Minnesota are hiring for Data Engineer Ml jobs?

Cities in Minnesota with the most Data Engineer Ml job openings:

Infographic showing various Data Engineer Ml job openings in Minnesota as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Senior Artificial Intelligence Data Engineer

Vizient

Minneapolis, MN

$110K - $150K/yr

Full-time

Re-posted 25 days ago


Key responsibilities

  • Build and support scalable data engineering solutions using Azure Databricks, PySpark, SQL, Delta Lake, Azure Data Factory, dbt, and Unity Catalog.

  • Develop reusable accelerators, templates, and standards for data transformation, deployment automation, and data product onboarding.

  • Transform source data into trusted, governed, and reusable derivative data products to support analytics, reporting, GenAI, semantic search, and emerging use cases.


Job description

When you're the best, we're the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future.

Summary

In this role, you will help build and enhance a modern, AI-ready data enablement platform that supports cross-domain analytics, governed data products, and reusable engineering patterns across the enterprise. You will enable data producers and analytics teams through guided pathways, reusable accelerators, metadata-driven frameworks, CI/CD patterns, Databricks Asset Bundles, dbt transformation standards, Unity Catalog governance, and Starburst/Trino analytical access. You will focus on helping teams create trusted, governed, reusable, and AI-ready derivative data products that support analytics, reporting, GenAI, semantic search, and emerging agentic platform use cases. As a hands-on senior individual contributor, you will combine platform engineering, data transformation, governance, and enablement to drive scalable and repeatable data product delivery across the organization.

Responsibilities

  • Build and support scalable data engineering solutions using Azure Databricks, PySpark, SQL, Delta Lake, Azure Data Factory, dbt, and Unity Catalog.
  • Improve metadata-driven Azure Data Factory and Databricks patterns for orchestration, configuration, monitoring, restartability, and operational support.
  • Develop reusable accelerators including CI/CD templates, Databricks Asset Bundle patterns, deployment automation, environment configuration, and data product onboarding templates.
  • Design, develop, and support dbt models, macros, tests, documentation, and transformation standards for governed analytical data products.
  • Provide guidance on appropriate technology selection and implementation patterns across dbt, Databricks notebooks and workflows, Delta Live Tables, Spark, and Starburst/Trino.
  • Support cross-domain analytics initiatives by transforming source-refined data into trusted, reusable, business-aligned derivative data products.
  • Leverage Unity Catalog to establish and support governed catalogs, schemas, tables, lineage, access controls, naming standards, and certification practices.
  • Support Starburst/Trino as an analytical and federated query layer for governed enterprise data consumption.
  • Apply Azure DevOps, Git, CI/CD, and Infrastructure as Code (IaC) practices to create repeatable, testable, and environment-aware platform delivery processes.
  • Troubleshoot and resolve production issues related to orchestration, transformations, data quality, access management, query performance, deployments, and operational workflows.
  • Collaborate with data engineering, analytics, platform, governance, and business teams to establish reusable, scalable, and supportable data engineering patterns.
  • Contribute to the evolution of enterprise data engineering standards, governance practices, observability capabilities, and AI-ready data product frameworks.

Qualifications

  • Relevant degree preferred.
  • 5 or more years of hands-on data engineering experience building production-grade data platforms, pipelines, or analytical data products required.
  • Strong experience with Azure Databricks, PySpark, Spark SQL, Delta Lake, Azure Data Factory, SQL, and dbt required.
  • Experience with Azure DevOps, Git, pull request workflows, CI/CD pipelines, and release management practices required.
  • Working knowledge of lakehouse architecture, metadata management, data governance, lineage, access control, and operational support required.
  • Demonstrated ability to function as a senior individual contributor with strong ownership, technical judgment, and cross-functional collaboration skills required.
  • Experience supporting enterprise-scale analytical platforms and governed data product delivery preferred.
  • Experience with Unity Catalog, Starburst/Trino, Pulumi or other Infrastructure as Code tools, Databricks Asset Bundles, Apache Iceberg concepts, and AKS/Kubernetes-based platform operations preferred.
  • Experience building reusable frameworks, accelerators, templates, or platform capabilities for engineering teams preferred.
  • Experience preparing governed structured data for AI/ML, GenAI, Retrieval-Augmented Generation (RAG), semantic search, copilots, or agentic workflows preferred.
  • Experience within healthcare, analytics, supply chain, finance, or other regulated enterprise environments preferred.
  • Strong problem-solving, communication, and collaboration skills with the ability to influence technical direction and establish best practices preferred.
  • You must be authorized to work in the United States without sponsorship.

#LI-JB1

Estimated Hiring Range:

At Vizient, we consider skills, experience, and organizational needs in our compensation approach. Geographic factors may adjust the range estimate and hires typically fall below the top range. Compensation decisions are tailored to individual circumstances. The current salary range for this role is $102,400.00 to $179,000.00.

This position is also incentive eligible.

Vizient has a comprehensive benefits plan! Please view our benefits here:

http://www.vizientinc.com/about-us/careers

Equal Opportunity Employer: Females/Minorities/Veterans/Individuals with Disabilities

The Company is committed to equal employment opportunity to all employees and applicants without regard to race, religion, color, gender identity, ethnicity, age, national origin, sexual orientation, disability status, veteran status or any other category protected by applicable law.