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Remote Qa Engineer Jobs in Virginia (NOW HIRING)

Anika Systems

Quality Assurance Engineer

Leesburg, VA · On-site +1

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 ...

Ad Hoc

Quality Assurance Engineer II (LOC)

Mclean, VA · Remote

Quality Assurance Engineer II Job number: 785 This is a remote position. Ad Hoc is a technology company that empowers organizations to deliver scalable, impactful digital services. Using modern ...

BayNova LLC

Quality Assurance Specialist

Arlington, VA · On-site +1

... remote / work from home role. This role performs functional and integration testing. The right ... The Quality Assurance Engineer will be responsible for designing and implementing tests, debugging ...

Ad Hoc

Quality Assurance Engineer III (LOC)

Mclean, VA · Remote

Quality Assurance Engineer III Job number: 783 This is a remote position. Ad Hoc is a technology company that empowers organizations to deliver scalable, impactful digital services. Using modern ...

Ad Hoc

Quality Assurance Engineer III (LOC)

Mclean, VA · Remote

Quality Assurance Engineer III This is a remote position. Ad Hoc is a technology company that empowers organizations to deliver scalable, impactful digital services. Using modern, agile methods, our ...

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All JobsQa Engineer JobsRemote Qa Engineer JobsRemote Qa Engineer Jobs in Virginia

Remote Qa Engineer information

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$18

$48

$77

How much do remote qa engineer jobs pay per hour?

As of Jun 12, 2026, the average hourly pay for remote qa engineer in Virginia is $48.13, according to ZipRecruiter salary data. Most workers in this role earn between $37.88 and $55.05 per hour, depending on experience, location, and employer.

How does a Remote QA Engineer effectively communicate and collaborate with development teams across different time zones?

Remote QA Engineers often work with development teams spread across various locations and time zones, which can create communication challenges. To ensure smooth collaboration, they typically rely on clear documentation, regular virtual meetings, and asynchronous communication tools like Slack or Jira. Establishing consistent testing processes and providing timely, detailed feedback helps bridge the gap and keeps projects on track. Additionally, being proactive in raising issues and participating in sprint planning or daily stand-ups fosters strong teamwork and project alignment.

What does a remote QA engineer do?

A remote QA (Quality Assurance) engineer is responsible for testing software applications to identify bugs, ensure functionality, and maintain quality standards, all while working from a remote location. They design test cases, execute manual and automated tests, and collaborate with development teams to resolve issues. Remote QA engineers use various tools to track defects, document results, and communicate with team members. Their goal is to ensure that software products are reliable, user-friendly, and meet customer requirements.

Is QA engineer still in demand?

QA engineers are still in demand as companies prioritize software quality assurance, especially with the growth of digital products and remote work. Skills in automation tools like Selenium and programming languages such as Python can enhance job prospects in this field.

Will QA testing be replaced by AI?

QA engineers play a crucial role in designing test cases, analyzing complex issues, and ensuring software quality, tasks that currently require human judgment. While AI tools can assist with automated testing and repetitive tasks, they do not fully replace the need for skilled QA professionals who interpret results and adapt testing strategies. Therefore, QA testing is expected to evolve with AI support rather than be completely replaced.

What is the difference between Remote Qa Engineer vs Remote Software Tester?

AspectRemote Qa EngineerRemote Software Tester
CredentialsQA certifications, testing tools knowledgeTesting certifications, basic QA knowledge
Work EnvironmentCollaborates with development teams, involved in automationFocuses on manual testing, bug reporting
Industry UsageUsed across tech, finance, healthcare sectorsCommon in software development companies
Search IntentLooking for QA roles with automation and testing skillsSearching for manual testing positions

Remote Qa Engineers typically have a broader role involving automation, test planning, and collaboration with development teams, while Remote Software Testers often focus on manual testing and bug identification. Both roles are essential in software quality assurance but differ in scope and responsibilities.

What Does a Remote QA Engineer Do?

As a remote QA engineer, you work from home while collaborating with software development teams to ensure bug-free and functional final products. Remote quality assurance engineering involves working with computer systems and software. As part of your responsibilities, you may test and analyze software performance, develop reports, virtually train personnel on software specifics, and otherwise work to provide flawless services and programs for organizations. You also troubleshoot and repair products, assess computer components via digital networks, and communicate with team members, clients, and superiors remotely. Other duties may require you to travel to job sites occasionally.

What engineers make $500,000?

Senior engineers in specialized fields such as software engineering, data engineering, or systems architecture can earn $500,000 or more annually, especially with extensive experience, advanced skills, and working in high-demand industries or companies. Compensation often includes base salary, bonuses, stock options, and other incentives.

Can a QA engineer work remotely?

Yes, many QA engineers work remotely, especially in roles that involve testing software, writing test cases, and using tools like Selenium or JIRA. Remote work arrangements are common in the industry and often require strong communication skills and familiarity with collaboration platforms.

What are the key skills and qualifications needed to thrive as a Remote QA Engineer, and why are they important?

To thrive as a Remote QA Engineer, you need strong analytical skills, a solid understanding of software testing methodologies, and typically a degree in computer science or a related field. Familiarity with testing tools like Selenium, JIRA, and CI/CD systems, as well as ISTQB certification, is highly beneficial. Excellent communication, self-motivation, and attention to detail are crucial soft skills for collaborating remotely and ensuring software quality. These abilities are essential for identifying issues early, maintaining high product standards, and effectively contributing to distributed teams.
More about Remote Qa Engineer jobs
What are the most commonly searched types of Qa Engineer jobs in Virginia? The most popular types of Qa Engineer jobs in Virginia are:
What are popular job titles related to Remote Qa Engineer jobs in Virginia? For Remote Qa Engineer jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Remote Qa Engineer jobs in Virginia look for? The top searched job categories for Remote Qa Engineer jobs in Virginia are:
What cities in Virginia are hiring for Remote Qa Engineer jobs? Cities in Virginia with the most Remote Qa Engineer job openings:
Remote Qa Engineer jobs near you
Infographic showing various Remote Qa Engineer job openings in Virginia as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $100,104 per year, or $48.1 per hour.
Quality Assurance Engineer

Quality Assurance Engineer

Anika Systems

Leesburg, VA • On-site, Remote

Full-time

Posted 19 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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