ZipRecruiter

Log In

1

Data Quality Assurance Engineer Jobs in Minnesota

SV Tech Systems Inc

QA Engineer

Eden Prairie, MN · On-site

Title : QA Engineer Location : Eden Prairie, MN Duration : 6 months C2H Required Skills ... Proven implementation experience in automated testing processes and tools Strong data management ...

Collabera

Quality Assurance Engineer

Eagan, MN

... QA patterns and practices, etc. Experience with RESTful services like SOAP Experience in Continuous Integration Experience working across multiple geographic locations and cultures Proficient ...

Collabera

Quality Assurance Engineer

Eagan, MN

Partner with software developers and other testers to deliver strategic product quality solutions. * Advocate for quality throughout the software development lifecycle. Qualifications Minimum ...

Collabera

Quality Assurance Engineer

Eagan, MN

Create and manage software quality assessments; including anticipating project needs and ... Java programming experience * Experience testing products deployed to Amazon Web Services (AWS)

Nextern

Senior Quality Assurance Engineer

Osseo, MN · On-site

Quality Assurance Engineers will also support development activities as they prepare to launch into ... Trend data and report findings at management review. · Operations Quality Support: o Act as the ...

next page

Showing results 1-20

All JobsData Quality Assurance Engineer JobsData Quality Assurance Engineer Jobs in Minnesota

Data Quality Assurance Engineer information

What does a data quality assurance engineer do?

A Data Quality Assurance Engineer is responsible for ensuring the accuracy, consistency, and reliability of data within an organization. They design and implement tests, validation processes, and quality checks to identify and resolve data errors or inconsistencies. Their work helps maintain high-quality data standards, which is crucial for informed decision-making and effective business operations. Additionally, they collaborate with data engineers and analysts to establish data quality metrics and best practices.

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

To thrive as a Data Quality Assurance Engineer, you need strong analytical skills, attention to detail, and a background in computer science or a related field. Familiarity with SQL, data profiling tools, automation frameworks, and certifications like ISTQB are commonly required. Excellent problem-solving, communication, and collaboration skills set top performers apart in this role. These competencies are vital for ensuring data integrity, driving process improvements, and supporting business decision-making with reliable information.

What are some common challenges faced by data quality assurance engineers when working with large datasets?

Data Quality Assurance Engineers often encounter challenges such as data inconsistency, incomplete records, and discrepancies across multiple data sources when handling large datasets. Ensuring data integrity requires meticulous validation and a strong understanding of both automated and manual testing techniques. Collaboration with data engineers, analysts, and business stakeholders is essential to identify root causes of quality issues and to implement effective solutions. Staying adaptable and detail-oriented helps address evolving data requirements and maintain high standards of data accuracy.

What is the difference between Data Quality Assurance Engineer vs Data Analyst?

AspectData Quality Assurance EngineerData Analyst
Primary FocusEnsuring data accuracy, integrity, and quality through testing and validation processesAnalyzing data to identify trends, generate reports, and support decision-making
Skills & CertificationsKnowledge of data testing tools, SQL, data management, and quality standardsProficiency in data analysis tools, SQL, Excel, and visualization software
Work EnvironmentOften part of data engineering or QA teams within IT or data departmentsTypically within business intelligence, marketing, or analytics teams

While both roles work with data, the Data Quality Assurance Engineer focuses on validating and maintaining data quality, whereas the Data Analyst interprets data to provide insights. They often collaborate but serve different functions in data management and analysis processes.

Is a Data Quality Assurance Engineer still in demand?

Yes, Data Quality Assurance Engineers are in demand due to the increasing importance of accurate and reliable data in organizations. They are needed to develop testing processes, validate data integrity, and work with tools like SQL and data management platforms, often requiring certifications or experience in data governance. The role is expected to grow as data-driven decision-making becomes more critical across industries.

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

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

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

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

What cities in Minnesota are hiring for Data Quality Assurance Engineer jobs?

Cities in Minnesota with the most Data Quality Assurance Engineer job openings:

Data Quality Assurance Engineer jobs near you

Senior Data Quality QA Engineer

Xoriant Corporation

Minneapolis, MN • On-site

Other

Posted 24 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.

Apply