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Remote Regression Testing Jobs in Frederick, MD (NOW HIRING)

Anika Systems

Quality Assurance Engineer

Leesburg, VA · On-site +1

This opportunity is 100% remote. Key Responsibilities Test Automation & QA Engineering * Design ... Build reusable testing utilities for data validation, regression testing, and pipeline ...

Maximus

System Test Engineer

Rockville, MD · Remote

This position is remote and requires an active Secret clearance or higher. Maximus TCS (Technology ... testing, targeted smoke test, targeted regression, and build support. #techjobs #clearance ...

Maximus

System Test Engineer

Frederick, MD · Remote

This position is remote and requires an active Secret clearance or higher. Maximus TCS (Technology ... testing, targeted smoke test, targeted regression, and build support. #techjobs #clearance ...

Maximus

System Test Engineer

Hagerstown, MD · Remote

This position is remote and requires an active Secret clearance or higher. Maximus TCS (Technology ... testing, targeted smoke test, targeted regression, and build support. #techjobs #clearance ...

Maximus

System Test Engineer

Gaithersburg, MD · Remote

This position is remote and requires an active Secret clearance or higher. Maximus TCS (Technology ... testing, targeted smoke test, targeted regression, and build support. #techjobs #clearance ...

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Remote Regression Testing information

See Frederick, MD salary details

$11

$51

$69

How much do remote regression testing jobs pay per hour?

As of May 31, 2026, the average hourly pay for remote regression testing in Frederick, MD is $51.15, according to ZipRecruiter salary data. Most workers in this role earn between $42.31 and $59.04 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Regression Testing professional, and why are they important?

To excel in Remote Regression Testing, you need a solid understanding of software testing principles, test case design, and experience with regression testing methodologies, often supported by a degree in computer science or a related field. Familiarity with automated testing tools such as Selenium, JUnit, or TestNG, as well as version control systems like Git, is typically required, and certifications like ISTQB can be advantageous. Strong analytical thinking, attention to detail, and clear communication skills are essential soft skills for collaborating remotely and identifying issues efficiently. These competencies ensure that software updates do not introduce new defects, maintaining product quality and reliability in distributed work environments.

What are some typical challenges faced by remote regression testers, and how can they be overcome?

Remote regression testers often encounter challenges such as communication barriers with development teams, time zone differences, and ensuring that test environments closely mirror production systems. To overcome these, it's important to establish clear communication channels—such as regular video meetings and detailed documentation—coordinate schedules for overlap with key team members, and use cloud-based testing tools that facilitate consistent and reproducible test environments. Proactively addressing these challenges helps ensure effective collaboration and high-quality testing outcomes.

What is remote regression testing?

Remote regression testing is a software testing process where testers validate that new changes or updates to an application have not adversely affected existing functionality, all while working from a remote location. This approach uses online collaboration tools, test automation frameworks, and cloud-based environments to execute and manage test cases. Remote regression testing is essential for distributed teams and organizations with flexible work arrangements, ensuring continuous software quality without the need for on-site presence. It supports efficient detection of bugs introduced by recent code changes, allowing teams to maintain high-quality standards in rapidly evolving software projects.

What is the difference between Remote Regression Testing vs Remote Test Automation Engineer?

AspectRemote Regression TestingRemote Test Automation Engineer
Primary FocusVerifying that recent code changes do not break existing functionalityDesigning, developing, and maintaining automated test scripts
Required SkillsManual testing, understanding of software features, basic scriptingProgramming, automation tools, scripting languages
Work EnvironmentTesting environments, collaboration with QA teamsDevelopment environments, automation frameworks
CertificationsISTQB, QA certificationsISTQB, automation testing certifications

Remote Regression Testing focuses on manual or semi-automated testing to ensure recent changes haven't introduced new issues, while Remote Test Automation Engineers develop and maintain automated testing frameworks to streamline testing processes. Both roles require testing knowledge but differ in technical depth and automation skills.

What are popular job titles related to Remote Regression Testing jobs in Frederick, MD? For Remote Regression Testing jobs in Frederick, MD, the most frequently searched job titles are:
What job categories do people searching Remote Regression Testing jobs in Frederick, MD look for? The top searched job categories for Remote Regression Testing jobs in Frederick, MD are:
What cities near Frederick, MD are hiring for Remote Regression Testing jobs? Cities near Frederick, MD with the most Remote Regression Testing job openings:
Remote Regression Testing jobs near you
Infographic showing various Remote Regression Testing job openings in Frederick, MD as of May 2026, with employment types broken down into 77% Full Time, and 23% Contract. Highlights an 100% Remote job distribution, with an average salary of $106,384 per year, or $51.1 per hour.
Quality Assurance Engineer

Quality Assurance Engineer

Anika Systems

Leesburg, VA • On-site, Remote

Full-time

Posted 7 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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