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Data Analytics Engineer Jobs in Minnesota (NOW HIRING)

Cargill

Senior Manufacturing Analytics Engineer

Wayzata, MN · On-site

$115K - $140K/yr

You will serve as a subject matter expert in applying data analytics to manufacturing processes and ... Collaborate with engineers across manufacturing and pilot facilities to design and deliver advanced ...

Cargill

Consultant, Data Analytics & Reporting

Wayzata, MN · On-site

$75K - $115K/yr

With limited supervision, this job collaborates with cross functional teams to ensure data accuracy and integrity performing data and statistical analysis using various programming languages. This ...

Cargill

Consultant, Data Analytics & Reporting

Wayzata, MN · On-site

$75K - $115K/yr

With limited supervision, this job collaborates with cross functional teams to ensure data accuracy and integrity performing data and statistical analysis using various programming languages. This ...

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Data Analytics Engineer information

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$43.6K

$127K

$173.8K

How much do data analytics engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for data analytics engineer in Minnesota is $127,046.00, according to ZipRecruiter salary data. Most workers in this role earn between $112,100.00 and $134,700.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Analytics Engineer, and why are they important?

To thrive as a Data Analytics Engineer, you need strong proficiency in data modeling, SQL, and statistical analysis, typically supported by a degree in computer science, statistics, or a related field. Familiarity with tools such as Python, R, Apache Spark, Tableau, and cloud data platforms like AWS or Google BigQuery is essential, along with relevant certifications. Excellent problem-solving, communication, and collaboration skills help you translate data insights into actionable business solutions. These skills and qualities are crucial for designing robust data pipelines and enabling data-driven decision-making across organizations.

How do Data Analytics Engineers typically collaborate with data scientists and business stakeholders on projects?

Data Analytics Engineers play a crucial role in bridging the gap between raw data and actionable insights by building, optimizing, and maintaining data pipelines. They often work closely with data scientists to ensure data is clean, accessible, and structured for advanced analytics or machine learning models. Additionally, they collaborate with business stakeholders to understand reporting requirements and ensure that data solutions align with organizational objectives. Regular communication and cross-functional teamwork are essential aspects of this role, as engineers must translate business needs into technical specifications and deliver reliable data products.

What does a Data Analytics Engineer do?

A Data Analytics Engineer designs, builds, and maintains the systems and infrastructure needed to collect, store, and analyze large sets of data. They work closely with data scientists, analysts, and business stakeholders to ensure data is accessible, reliable, and organized for analysis. Their responsibilities typically include building data pipelines, optimizing database performance, and ensuring data quality and security. Data Analytics Engineers play a crucial role in transforming raw data into actionable insights that drive business decisions.

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

AspectData Analytics EngineerData Scientist
CredentialsBachelor's or master's in CS, Data Science, or related fields; certifications like Google Data AnalyticsBachelor's or master's in CS, Statistics, or related fields; certifications like Certified Data Scientist
Work EnvironmentFocus on building data pipelines, dashboards, and analytics toolsFocus on statistical modeling, machine learning, and data exploration
Employer & Industry UsageUsed across tech, finance, healthcare for data infrastructure and analyticsCommon in research, product development, and advanced analytics teams

While both roles work with data, Data Analytics Engineers primarily develop data infrastructure and tools for analysis, whereas Data Scientists focus on statistical modeling and machine learning to generate insights. They often collaborate but have distinct technical focuses.

What are the most commonly searched types of Data Analytics Engineer jobs in Minnesota? The most popular types of Data Analytics Engineer jobs in Minnesota are:
What are popular job titles related to Data Analytics Engineer jobs in Minnesota? For Data Analytics Engineer jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Data Analytics Engineer jobs in Minnesota look for? The top searched job categories for Data Analytics Engineer jobs in Minnesota are:
What cities in Minnesota are hiring for Data Analytics Engineer jobs? Cities in Minnesota with the most Data Analytics Engineer job openings:
Data Analytics Engineer jobs near you

Digital Analytics Engineer

Purple Drive Technologies

Minneapolis, MN • On-site

Full-time

Posted 19 days ago

Job description

Overview:
Experience requested: 5+ YRS
ROLE: Digital Analytics Engineer
Description:
Adobe Analytics, of front-end development experience, with functional knowledge of web technologies (e.g., HTML, CSS) and client-side programming languages (e.g., JavaScript, iOS, Android).
"Key Responsibilities
• Collaborate with software developers, product managers, UI designers, and data scientists to develop consumer tracking and analytics solutions across all U.S. Bank digital channels.
• Troubleshoot and resolve technical and engineering issues related to the setup and configuration of analytics tools and reports.
• Define appropriate data models and implement instrumentation for collecting and analyzing analytics data.
• Create documentation for tools and libraries within the analytics platform.
• Promote repeatable best practices that empower software engineers to independently implement analytics solutions.
Required Qualifications
• Bachelor's degree in computer science or engineering.
• 5+ years of front-end development experience, with functional knowledge of web technologies (e.g., HTML, CSS) and client-side programming languages (e.g., JavaScript, iOS, Android).
• Official Adobe Analytics Expert Developer Certification and Tealium EventStream iQ Tag Management Certification required.
• Additional certifications required for various digital analytics role, including but not limited to:
• Tealium EventStream API Hub Certification
• Adobe Customer Journey Analytics Developer Expert Certification
• 2+ years of practical implementation experience with one or more web/digital analytics tools (e.g., Adobe Analytics, Google Analytics) and tag management systems (e.g., Tealium, Ensighten, Adobe Launch, Google Tag Manager).
• Basic SQL skills for data troubleshooting."

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