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Data Qa Jobs in Virginia (NOW HIRING)

Anika Systems

Quality Assurance Engineer

Leesburg, VA · On-site +1

Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using Python and SQL. * Build reusable testing utilities for data validation, regression ...

TMCI

Be Seen First

Data Quality Assurance Analyst [TMC-013-001]

Mclean, VA · On-site

$85K - $130K/yr

TMCI is seeking a Data Quality Assurance Analyst (DQAA) to support execution of enterprise data quality practices. This role focuses on implementing and monitoring data quality rules, performing data ...

PHARMALOGIC HOLDINGS

QA Manager

Salem, VA · On-site

$75K - $100K/yr

... data quality and integrity, ANDA documentation and other routine QA functions. The site QA is responsible for training documentation for the staff on QA related activities. The site QA is the ...

New

AaraTechnologies Inc

QA ENGINEER

Virginia Beach, VA · Remote

QA Analyst Healthcare (2 3 Years Experience) Company: AaraTech Inc About the Role AaraTech Inc is ... Validate healthcare data and reporting outputs * Identify, log, track, and retest defects * Support ...

CRYOPAK INC

QA Technician

Roanoke, VA · On-site

Our data loggers are principally used for cold chain management solutions to ensure that food ... Our QA Technician supports quality and validation functions. This role requires basic to ...

Helioz Solutions INC

QA Analyst

Richmond, VA · On-site

An EXPERIENCED QA Analyst is needed to design test scenarios, identify test data, create test cases, execute test cases, identify/report defects, verify defects and participate in the overall testing ...

Helioz Solutions INC

QA Analyst

Richmond, VA

An EXPERIENCED QA Analyst is needed to design test scenarios, identify test data, create test cases, execute test cases, identify/report defects, verify defects and participate in the overall testing ...

Orbis

Quality Assurance Specialist

Arlington, VA · On-site

The QA Specialist will play a pivotal role in verifying the accuracy, completeness, and compliance ... Advanced user of Microsoft Excel (pivot tables, macros, data modeling, dashboarding). * Proficient ...

Orbis

Quality Assurance Specialist

Mclean, VA · On-site

The QA Specialist will play a pivotal role in verifying the accuracy, completeness, and compliance ... Advanced user of Microsoft Excel (pivot tables, macros, data modeling, dashboarding). * Proficient ...

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Data Qa information

What are top 3 skills for a QA analyst?

A QA analyst should have strong attention to detail, proficiency in testing tools and scripting languages, and good understanding of software development life cycle processes. Effective communication skills and the ability to document defects clearly are also essential. Familiarity with automation testing and quality standards enhances their effectiveness in ensuring software quality.

Is data quality a good career?

Data quality is a valuable career path within data management, focusing on ensuring the accuracy, consistency, and reliability of data. Data QA professionals often use tools like SQL and data validation software, and the role can lead to opportunities in data analysis, data governance, and analytics. It offers steady demand as organizations increasingly rely on high-quality data for decision-making.

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 QA still in demand?

Quality Assurance (QA) roles, including Data QA, remain in demand as companies prioritize data accuracy and software quality. Skills in testing tools, scripting, and understanding data workflows are valuable, and demand is expected to grow with increasing reliance on data-driven decision-making.

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, validate data quality against standards, and often use tools like SQL or data validation software to perform their tasks, supporting reliable data-driven decision-making.
What are popular job titles related to Data Qa jobs in Virginia? For Data Qa jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Data Qa jobs in Virginia look for? The top searched job categories for Data Qa jobs in Virginia are:
What cities in Virginia are hiring for Data Qa jobs? Cities in Virginia with the most Data Qa job openings:
Data Qa jobs near you
Infographic showing various Data Qa job openings in Virginia as of June 2026, with employment types broken down into 84% Full Time, 14% Part Time, and 2% Contract. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution.
Quality Assurance Engineer

Quality Assurance Engineer

Anika Systems

Leesburg, VA • On-site, Remote

Full-time

Posted 21 days ago

Job description

Anika Systems is seeking a highly technical Quality Assurance Engineer with strong development, SQL, and Python expertise to support enterprise data platforms for federal clients. This is not a traditional manual QA role and this position requires a developer mindset, focused on automation, data validation, and platform reliability across modern cloud-based architectures.
The ideal candidate will design and implement automated testing frameworks for ETL pipelines, Apache Iceberg data architectures, XBRL datasets, and performance-optimized structures such as materialized views-ensuring data accuracy, integrity, and trust across the enterprise. This role also requires proficiency in AI tools and AI-driven workflows, leveraging automation and intelligent testing techniques to improve quality and delivery speed.
This opportunity is 100% remote.
Key Responsibilities
Test Automation & QA Engineering
  • Design, develop, and maintain automated QA frameworks for data pipelines, APIs, and analytics platforms using Python and SQL.
  • Build reusable testing utilities for data validation, regression testing, and pipeline certification.
  • Integrate automated tests into CI/CD pipelines to support continuous testing and deployment.
  • Develop unit, integration, and end-to-end test cases for complex data workflows.
  • Leverage AI-assisted testing tools to generate test cases, identify edge cases, and improve test coverage.
Data Validation & ETL Testing
  • Validate ETL/ELT pipelines to ensure accurate ingestion, transformation, and delivery of data.
  • Create automated checks for data completeness, consistency, accuracy, and timeliness.
  • Test ingestion and transformation of complex datasets, including XBRL financial data.
  • Implement reconciliation and audit mechanisms across source-to-target mappings.
  • Apply AI-driven anomaly detection to identify data quality issues and pipeline failures.
Iceberg & Materialized View Testing
  • Develop and execute test strategies for Apache Iceberg-based data lakehouse architectures, including:
    • Schema evolution validation
    • Time travel and versioning accuracy
    • Partitioning and performance behavior
  • Validate and compare materialized views vs. Iceberg table performance and consistency, including:
    • Query performance benchmarking
    • Data freshness and latency
    • Storage efficiency and maintenance overhead
  • Ensure alignment between precomputed datasets (materialized views) and underlying source data.
Data Quality, Metadata & Context Validation
  • Implement automated validation for data quality rules, lineage, and metadata accuracy.
  • Support context engineering by validating that datasets include proper business context, definitions, and relationships.
  • Integrate QA processes with enterprise data catalogs and metadata systems to ensure discoverability and trust.
  • Validate AI-generated metadata, lineage, and transformations for accuracy and traceability.
AI-Driven Quality Engineering
  • Apply AI/ML and generative AI tools to enhance QA processes, including intelligent test generation, defect prediction, and automated root cause analysis.
  • Validate data readiness for AI/ML and generative AI use cases, ensuring datasets meet quality, completeness, and governance standards.
  • Collaborate with data and AI teams to test data pipelines supporting RAG, analytics, and machine learning workflows.
  • Ensure alignment with responsible AI practices, including traceability, explainability, and data integrity.
OCDO & Data Strategy Support
  • Support enterprise data management programs and OCDO initiatives by ensuring data quality and reliability across systems.
  • Contribute to data maturity assessments by evaluating data quality, testing coverage, and governance adherence.
  • Align QA processes with Federal Data Strategy and Evidence Act requirements.
Stakeholder Collaboration & Agile Delivery
  • Work closely with data engineers, data architects, and analysts to define test strategies and acceptance criteria.
  • Participate in stakeholder engagement sessions and listening campaigns to understand data quality expectations and pain points.
  • Document test results, defects, and quality metrics for both technical and non-technical stakeholders.
  • Operate within Agile teams to iteratively improve data quality processes and tooling.
  • Promote adoption of AI-driven efficiencies and automation across QA and data engineering workflows.
Required Qualifications
  • Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
  • 5+ years of experience in QA engineering, data testing, or software development.
  • Strong programming skills in Python and advanced proficiency in SQL.
  • Experience building automated test frameworks for data platforms and ETL pipelines.
  • Hands-on experience with:
    • AWS data services (S3, Glue, Redshift, Lambda, etc.)
    • Apache Iceberg or similar data lake technologies
  • Experience validating materialized views and performance-optimized data structures.
  • Familiarity with XBRL or complex financial/regulatory datasets.
  • Understanding of data modeling, metadata, and data governance principles.
  • Experience with CI/CD tools and automated testing integration.
  • Demonstrated proficiency with AI tools and AI-assisted development/testing workflows.
  • Understanding of data quality requirements for AI/ML and analytics use cases.
  • U.S. Citizenship required; ability to obtain and maintain a federal clearance.
Preferred Qualifications
  • Experience supporting federal agencies such as SEC, DHS, Treasury, or Federal Reserve System.
  • Familiarity with data catalog and governance tools (e.g., Collibra, Alation, ServiceNow).
  • Experience with Apache Spark or distributed data processing frameworks.
  • Knowledge of data quality tools and observability platforms.
  • Exposure to data maturity frameworks (e.g., EDM DCAM, TDWI).
  • Experience testing large-scale cloud data platforms and lakehouse architectures.
  • Experience validating data pipelines supporting AI/ML, analytics, or generative AI solutions.
  • Familiarity with AI-driven testing tools or frameworks.

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