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Data Qa Jobs in Minnesota (NOW HIRING)

Georgia IT, Inc.

QA Lead

Minneapolis, MN · On-site

The role involves coaching and mentoring QA resources, collaborating with IT Business Teams, and ... data flows to help the team with set up of test scenarios data when needed • Understand job ...

Stem Xpert

QA Lead

Eagan, MN

JOB : QA Lead LOCATION : Eagan MN DURATION: 6+ Months Client : Airlines Industry Mode of interview ... data when needed Reviews requirements for testability, ambiguity, or redundancy Ensures ...

CoreCivic

Manager, Quality Assurance

Appleton, MN · On-site

$79K - $83K/yr

Manages all areas of Quality Assurance to include audits, inspections, data gathering and analysis, reporting, training and communication in accordance with established policies and procedures and ...

Trioptus

Quality Assurance Lead

Saint Paul, MN · On-site

Familiarity with performance testing, test data management, and vendor validation. * Working knowledge of automation frameworks or tools. * Certifications such as QA, PMP, CSM, SAFe, or related. Core ...

CoreCivic

Manager, Quality Assurance

Appleton, MN · On-site

Manages all areas of Quality Assurance to include audits, inspections, data gathering and analysis, reporting, training and communication in accordance with established policies and procedures and ...

EVERIDGE

Quality Assurance Supervisor

Winnebago, MN · On-site

Plan, direct, and coordinate the activities of the quality assurance department. * Develop ... Posts and maintains forms and charts of inspection data, quality levels, or special quality studies.

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Data Qa information

What is the difference between Data Qa vs Data Analyst?

AspectData QaData Analyst
Required CredentialsBasic understanding of testing tools, some certificationsDegree in data-related fields, certifications like Microsoft or SAS
Work EnvironmentQuality assurance teams, software testing environmentsData analysis teams, business intelligence settings
Employer & Industry UsageTech companies, software development firmsFinance, marketing, healthcare, and other industries
Common Search & ComparisonOften compared for data quality rolesMore focused on data insights and reporting

Data Qa professionals primarily focus on testing and ensuring data quality, often working within QA teams to validate data accuracy and integrity. Data Analysts, on the other hand, analyze data to generate insights, create reports, and support decision-making. While both roles work with data, Data Qa emphasizes quality assurance processes, whereas Data Analysts focus on data interpretation and analysis.

Is data QA a difficult job?

Data QA (Quality Assurance) involves reviewing and testing data for accuracy, consistency, and completeness, which can require attention to detail and analytical skills. The difficulty depends on the complexity of the data, the tools used, and the specific requirements of the role, but it generally involves systematic processes and knowledge of data management practices.

What does a data QA do?

A data QA (Quality Assurance) specialist reviews and tests data to ensure accuracy, consistency, and completeness. They identify and report data issues, often using tools like SQL or data validation software, to maintain data quality for analysis and decision-making.

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

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

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

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

Data Qa jobs near you
Infographic showing various Data Qa job openings in Minnesota as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 3% Contract, and 1% Nights. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Senior Data Quality QA Engineer

Xoriant Corporation

Minneapolis, MN • On-site

Other

Re-posted 2 days ago

Job description

Job Title: Senior Data Quality QA Engineer

Location: Minneapolis, MN (Hybrid, 3 days /week onsite)

Duration: Long Term Contract

Description:

The candidate should have hands-on experience with Databricks, AWS Glue, Amazon Redshift, SQL, Python, and cloud-based data platforms. Experience testing data migration, data transformation, reporting, and AI-enabled development workflows is highly preferred.

Responsibilities

  • Design and execute end-to-end test strategies for data engineering and analytics projects.
  • Validate ETL/ELT pipelines built using AWS Glue and Databricks.
  • Perform data quality testing across multiple data sources and data warehouses.
  • Develop SQL queries to validate source-to-target data transformations.
  • Verify data completeness, consistency, accuracy, duplicates, and reconciliation.
  • Perform testing of Amazon Redshift data warehouse solutions.
  • Validate batch and incremental data loads.
  • Create automated data validation frameworks using Python and SQL.
  • Work closely with Data Engineers, AI Developers, Product Owners, and Business Analysts.
  • Review business requirements and create detailed test scenarios and test cases.
  • Identify, document, and track defects through resolution.
  • Perform regression, integration, system, and user acceptance testing.
  • Support CI/CD pipelines and automated testing processes.
  • Use AI-assisted development tools (such as GitHub Copilot, Amazon Q, or similar AI coding assistants) to improve test automation, SQL generation, and productivity.
  • Ensure data governance, data lineage, and quality standards are maintained.
  • Participate in sprint planning, backlog grooming, and Agile ceremonies.
  • Prepare test execution reports and quality metrics for stakeholders.

Required Skills

  • 7–8 years of experience in Software QA with strong focus on Data Quality Testing.
  • Strong SQL skills with experience writing complex queries.
  • Hands-on experience with Databricks.
  • Experience testing AWS Glue ETL pipelines.
  • Experience validating data in Amazon Redshift.
  • Strong knowledge of ETL/ELT testing methodologies.
  • Experience with Python for test automation and data validation.
  • Good understanding of cloud platforms, preferably AWS.
  • Experience validating structured and semi-structured data (CSV, JSON, Parquet, Delta).
  • Knowledge of data reconciliation and source-to-target validation.
  • Experience using defect management tools such as Jira.
  • Experience with Git and CI/CD pipelines.
  • Strong analytical and troubleshooting skills.
  • Excellent communication and documentation skills.

Preferred Skills

  • Experience with AI-enabled software development and testing.
  • Experience using GitHub Copilot, Amazon Q, ChatGPT, or similar AI tools.
  • Knowledge of Apache Spark and PySpark.
  • Experience with Delta Lake.
  • Familiarity with Airflow or other workflow orchestration tools.
  • Experience with Tableau, Power BI, or QuickSight report validation.
  • Exposure to data governance and data quality tools.
  • Experience working in Agile/Scrum environments.

Nice to Have

  • Experience with machine learning data validation.
  • Knowledge of Great Expectations or similar data quality frameworks.
  • AWS Certification or Databricks Certification.
  • Experience with large-scale enterprise data migration projects.

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