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Data Engineer Contract Jobs in Minnesota (NOW HIRING)

Limelight Health

Senior Data Engineer

Minneapolis, MN · On-site

$110K - $150K/yr

Partner across teams: work with upstream producers on source changes and contracts, with downstream ... AWS-native data engineering across streaming, processing, storage, and catalog services (e.g. MSK ...

Avalore, LLC

Chief Data/AI Engineer

Virginia, MN · On-site

$97K - $181K/yr

The Chief Data/AI Engineer will oversee all enterprise data engineering and operations support ... contract-specific affordability and organizational requirements. * Salary estimates for this job ...

Randstad Digital

Engineer

Minneapolis, MN · On-site

$91.10 - $96.10/hr

Contract salary: $91.10 - 96.10 per hour work hours: 8am to 5pm education: Bachelors responsibilities: * Seeking a senior, hands-on data engineer to design and deliver trusted data products for ...

Judge Group, Inc.

Software Engineer

Minneapolis, MN · On-site

$64 - $69/hr

Duration: 12-Month Contract Location: Minneapolis, MN (Preferred) | Dallas, TX | Charlotte, NC ... Analyze platform health metrics, reporting data, and trends to identify recurring issues and ...

Capgemini

Embedded Hardware Test Engineer

Saint Paul, MN · On-site

$22.19 - $34.67/hr

Embedded Hardware Test Engineer (Contract) Saint Paul, MN, United States (On-site) Contract (3 months) Published 4 days ago * Sensor Interfaces * Data Analysis & Results Evaluation * root cause ...

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Showing results 21-40

All JobsData Engineer Contract JobsData Engineer Contract Jobs in Minnesota

Data Engineer Contract information

See Minnesota salary details

$43.6K

$127K

$173.8K

How much do data engineer contract jobs pay per year?

As of Sep 14, 2026, the average yearly pay for data engineer contract 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 is a data engineer contract?

A Data Engineer Contract job is a temporary or project-based role where a data engineer is hired for a specific duration to design, build, and maintain data pipelines and infrastructure. Contract data engineers often work with big data technologies, ETL processes, and cloud platforms to ensure data is efficiently processed and accessible. These roles can be short-term (a few months) or long-term (a year or more), depending on the project's needs. Contractors may work independently or as part of a larger data team, and they are typically paid hourly or per project rather than receiving a fixed salary and benefits like full-time employees.

What are the typical daily responsibilities of a data engineer contract?

As a Data Engineer contractor, your day-to-day tasks often include designing, building, and maintaining data pipelines, implementing ETL processes, and preparing datasets for analytics or machine learning teams. You may be asked to collaborate with data scientists, analysts, and other engineers to understand data requirements and resolve technical issues. Contractors also frequently assess data quality, optimize performance, and document their work for seamless team integration. The role is fast-paced and may require you to quickly adapt to new projects or technologies, making it ideal for those who enjoy dynamic, project-based environments.

What are the key skills and qualifications needed to thrive in the data engineer contract position, and why are they important?

To thrive as a Data Engineer Contract, you need expertise in data modeling, ETL processes, and proficiency with programming languages such as Python or SQL, often supported by a degree in computer science or related field. Familiarity with big data platforms like Hadoop or Spark, experience with cloud services (AWS, GCP, or Azure), and certifications in relevant technologies are highly valued. Strong problem-solving skills, effective communication, and the ability to work independently are crucial soft skills. These abilities ensure data engineers can efficiently design scalable pipelines, troubleshoot issues, and collaborate across teams to support data-driven decision-making.

What are the most commonly searched types of Data Engineer jobs in Minnesota?

The most popular types of Data Engineer jobs in Minnesota are:

What are popular job titles related to Data Engineer Contract jobs in Minnesota?

For Data Engineer Contract jobs in Minnesota, the most frequently searched job titles are:

What job categories do people searching Data Engineer Contract jobs in Minnesota look for?

The top searched job categories for Data Engineer Contract jobs in Minnesota are:

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

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

Data Engineer Contract jobs near you
Infographic showing various Data Engineer Contract job openings in Minnesota as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 82% Full Time, 14% Part Time, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $127,046 per year, or $61.1 per hour.

Data Engineer (SQL, ETL, Snowflake)

Arden Hills, MN • On-site

Prudent Technologies and Consulting
1 - 10 employees
Learn more about Prudent Technologies and Consultingexternal

$120K - $144K/yr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Role: Data Engineer (SQL, ETL, Snowflake)
Location: Arden Hills, MN (Onsite )
Duration:  Contract

Experience : 10+ Years

Job Summary: The Data Engineer will build reliable, scalable data solutions that strengthen trusted HR and enterprise analytics. This role works closely with business analysts, lead data engineers, and the HR team on site to deliver well-managed data pipelines, improve data quality, and improve data-driven operations across the organization.

Competencies-Skills (Required):

Core Experience: 7+ years in SQL, data engineering, and data modeling.
Data Platforms: Builds and supports data warehouses, data lakes, and lake house environments.
Snowflake: Minimum 2 years of hands-on Snowflake experience.
Delivery Leadership: Leads full-lifecycle data engineering or reporting initiatives.
Data Ingestion: Designs and builds ingestion patterns for files, APIs, databases, CDC, replication, and streaming/message-based data sources.
Data Pipelines: Builds, optimizes, and operates reliable ETL/ELT pipelines and integrated datasets.
DevOps: Uses CI/CD, automated testing, and deployment practices for data solutions.
Scripting: Uses scripting languages, preferably Python, for data engineering automation.
Ownership: Works independently, manages priorities, and drives outcomes with minimal supervision.
Problem Solving: Applies strong analytical, troubleshooting, and root cause analysis skills.
Communication: Communicates clearly and coordinates effectively with technical and business partners.
Data Security: Applies security practices such as encryption, anonymization, masking, and access-aware design.
Modern Data Architecture: Understands warehouse, lake, lake house, and cloud-based data architecture patterns.
Azure Familiarity: Understands Azure services used for data storage, integration, processing, orchestration, and security.
Orchestration: Manages pipeline orchestration, scheduling, monitoring, and data flow reliability.
Metadata Management: Uses metadata-driven practices to improve usability, lineage, and governance.
Version Control: Uses version control to support quality, traceability, and team collaboration.
Scalability: Designs pipelines with scalability, performance, and distributed processing considerations.

Competencies-Skills (Preferred):

Advanced Platform Optimization: Optimizes complex Snowflake and Databricks/Spark workloads, including streams, tasks, dynamic tables, and performance tuning.
HR Data Experience: Works with HR systems such as Workday and supports workforce analytics, employee lifecycle reporting, and people data use cases.
HR Data Governance: Applies privacy, minimization, masking, and access practices specifically for confidential employee and workforce data.
Enterprise Solution Design: Shapes reusable data product patterns, logical models, and target-state designs for enterprise analytics.
Power BI Enablement: Partners with analysts to support semantic models, curated datasets, dashboards, and trusted reporting experiences.
Data Vault: Understands Data Vault modeling concepts and architecture.
Practical Innovation: Identifies pragmatic opportunities to improve data products, analytics delivery, and user adoption.
Agentic AI Exposure: Understands agentic AI concepts and opportunities to apply AI-enabled workflows in data and analytics contexts.
AI Productivity: Uses AI tools responsibly to improve personal productivity, streamline analysis, accelerate documentation, and support delivery quality.
MLOps Exposure: Understands machine learning operations and production model lifecycle concepts.

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