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Data Quality Assurance Jobs (NOW HIRING)

NAVA Software Solutions

QA Engineer

Houston, TX · On-site

NAVA Software solutions is looking for a QA Engineer Details: QA Engineer Location: Houston, TX (2 ... Focused on Data Validation * Strong SQL skills * Any tools for data quality/ validation

Virtusa

Quality Assurance Lead

Minneapolis, MN · On-site

Quality Assurance Lead Workstream: Operational Excellence & Offshore Delivery Role Purpose Owner of the testing and validation of data products, ensuring that the data is accurate, reliable, and fit ...

Virtusa

Quality Assurance Lead

Minneapolis, MN

Quality Assurance Lead Workstream: Operational Excellence & Offshore Delivery Role Purpose Owner of the testing and validation of data products, ensuring that the data is accurate, reliable, and fit ...

Mindlance

QA Analyst or Data Tester

Washington, DC · On-site

Title: QA Analyst or Data Tester Duration: 12 Months - Long Term Location: Washington, DC 20433 (4 days onsite from day 1) Overview: Responsible for independently verifying that data transformations ...

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How much do data quality assurance jobs pay per hour?

As of Jul 4, 2026, the average hourly pay for data quality assurance in the United States is $44.87, according to ZipRecruiter salary data. Most workers in this role earn between $36.54 and $54.57 per hour, depending on experience, location, and employer.

Is QA analyst an entry level job?

A QA analyst role can be entry level or require experience, depending on the organization. Entry-level positions typically focus on basic testing and quality checks, often requiring familiarity with testing tools and attention to detail. More advanced roles may demand knowledge of scripting, automation, or industry-specific standards.

Will AI replace data analyst?

AI is transforming data analysis by automating routine tasks and enhancing data processing capabilities, but it is unlikely to fully replace data analysts. Instead, data analysts will increasingly focus on interpreting insights, developing strategies, and managing AI tools, requiring skills in data visualization, statistical analysis, and domain knowledge.

Is data quality a good career?

Data Quality Assurance is a valuable career that involves ensuring the accuracy, consistency, and reliability of data within organizations. It often requires skills in data analysis, attention to detail, and familiarity with tools like SQL or data validation software, making it a stable and in-demand field with opportunities for growth.

What are some typical daily responsibilities of a Data Quality Assurance professional?

A Data Quality Assurance professional's day often includes creating and executing test plans, validating datasets, identifying data anomalies, and collaborating with data engineers or analysts to resolve quality issues. They routinely monitor data pipelines, document test results, and help develop or refine data quality standards and processes. Teamwork and regular communication with IT, business stakeholders, and data governance teams are common parts of the role. These responsibilities help ensure data integrity across projects and support an organization's overall data-driven goals.

What does a data QA do?

A data QA (Quality Assurance) professional reviews and tests data to ensure accuracy, consistency, and completeness. They identify and resolve data issues, often using tools like SQL or data validation software, to maintain high data quality standards essential for decision-making and reporting.

What is a Data Quality Assurance job?

A Data Quality Assurance (QA) job involves ensuring the accuracy, consistency, and reliability of data within an organization. Professionals in this role develop and implement data validation processes, identify and fix inconsistencies, and work with teams to maintain data integrity. They use tools and frameworks to automate quality checks, perform audits, and enforce data governance policies. The goal is to support data-driven decision-making by providing stakeholders with high-quality, trustworthy data.

What are the key skills and qualifications needed to thrive in the Data Quality Assurance position, and why are they important?

To thrive as a Data Quality Assurance professional, you need a solid understanding of data management principles, data validation techniques, and strong analytical skills, often backed by a degree in computer science, information systems, or a related field. Familiarity with data quality tools (such as Informatica or Talend), SQL, and experience with database systems are typically required, and certifications in data management can be a plus. Attention to detail, problem-solving abilities, and effective communication are important soft skills for effectively identifying and resolving data issues. These competencies ensure the reliability and accuracy of organizational data, supporting sound decision-making and operational efficiency.

More about Data Quality Assurance jobs
What cities are hiring for Data Quality Assurance jobs? Cities with the most Data Quality Assurance job openings:
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Data Quality Assurance jobs near you
Infographic showing various Data Quality Assurance job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, 1% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $93,338 per year, or $44.9 per hour.

QA Lead Data Engineering - Long Term Contract

Rootshell Enterprise Technologies, Inc.

Pasadena, CA • On-site

$140K/yr

Contractor

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

Job description

QA Lead - Data Engineering
Pasadena, CA
Long Term Contract
We are seeking a highly skilled QA Lead - Data Engineering to define and implement robust QA strategies, automated testing frameworks, and data quality assurance processes. The ideal candidate will have expertise in Google Cloud Platform (GCP), Databricks, ETL pipelines, SQL, Python scripting, and CI/CD workflows. This role requires a strong understanding of data governance, data validation, and test automation to ensure high-quality data engineering solutions.
QA Strategy and Leadership:
* Define and implement robust OA strategies, methodologies, and processes for data engineering projects.
* Lead and mentor a team of QA engineers, ensuring alignment with best practices and project goals.
* Collaborate with stakeholders to understand data quality requirements and translate them into test plans and case
Testing Frameworks and Automation:
* Develop and maintain automated testing frameworks for ETL pipelines, data lakes, and data ware holises on Google cloud platform, Databricks, and related services.
* Design data validation and verification processes to ensure the accuracy and consistency of data across pipelines.
* Automate regression, performance, and integration testing to minimize manual efforts.
Data Quality Assurance:
* Conduct root cause analysis for data quality issues and drive corrective actions.
* Ensure compliance with data governance policies and data security best practices.
* Develop data profiling and monitoring dashboards using tools like Google
* Monitor, Power BI, or custom solutions.
Technical Skills and Qualifications:
* GCP Expertise: Proficient in GCP, Dataflow, BigQuery, Cloud composer, and google Storage services.
* Databricks: Experience with Spark-based data engineering workflows, including Delta Lake.
* Testing Tools & Frameworks: Strong experience with tools like PyTest, dbt (data build tool), or similar testing frame works for data pipelines.
* SQL and Scripting: Advanced SQL skills for data validation with Python proficiency for automation.
* Big data Knowledge: Knowledge of Big Data processing and distributed computing. Understand the importance data modeling concepts
* CI/CD: Knowledge of CI/CD pipelines on or GitHub Actions, focusing on data engineering workflows.
* Data Governance: Familiarity with data governance tools and concepts such as metadata management, lineage, and data cataloging.
* Communication: Excellent communications skills, with the ability to synthesize, simplify and explain complex problems to different tunes of audience including executives

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