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Qa Architect Remote Jobs (NOW HIRING)

Anika Systems

Quality Assurance Engineer

Leesburg, VA · On-site +1

This opportunity is 100% remote. Key Responsibilities Test Automation & QA Engineering * Design ... Work closely with data engineers, data architects, and analysts to define test strategies and ...

Anika Systems

Quality Assurance Engineer

Leesburg, VA · Remote

This opportunity is 100% remote. Key Responsibilities Test Automation & QA Engineering * Design ... Work closely with data engineers, data architects, and analysts to define test strategies and ...

Mitsui Chemical America

QA Manager

New York, NY · Remote

$88K - $130K/yr

Location: Remote, US Department: QA/RC - Corporate Planning Division Reports To: QA/RC Director Are you ready to elevate product quality and reliability? We are seeking an experienced ...

ITRADE STEM

Lead Quality Assurance Engineer

Miami, FL · Remote

We seek a Lead Quality Assurance Engineer for an exciting remote opportunity based in Miami. Our ... Experience with distributed systems testing and microservices architecture * Knowledge of API ...

Axiom

Data Architect (Remote)

Boston, MA · On-site +1

$69.25 - $89/hr

Data Architect 1 - US Remote About Axiom: As the leading alternative legal services provider ... Establish and enforce data governance policies, data quality standards, and associated assurance ...

Octagon Talent

Lead Quality Assurance Engineer

Miami, FL · Remote

$100K - $160K/yr

JOB OVERVIEW: We are looking for a Lead Quality Assurance Engineer for an exciting remote ... Experience with distributed systems testing and microservices architecture * Knowledge of API ...

Agero, Inc.

Sr. QA Engineer

... a fully remote environment. Responsibilities : • Develop and maintain robust automation ... architecture discussions to identify risks and provide technical context for quality trade-offs ...

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Qa Architect Remote information

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How much do qa architect remote jobs pay per hour?

As of Jun 10, 2026, the average hourly pay for qa architect remote in the United States is $82.35, according to ZipRecruiter salary data. Most workers in this role earn between $57.69 and $113.94 per hour, depending on experience, location, and employer.

How does a QA Architect collaborate with development and product teams in a remote work setting?

As a QA Architect working remotely, collaboration with development and product teams typically involves a mix of scheduled video meetings, asynchronous communication via tools like Slack or Jira, and documentation sharing through platforms like Confluence. QA Architects are often responsible for defining quality standards and test automation strategies, which requires close coordination to align testing frameworks with development cycles. Regular syncs and clear documentation help ensure everyone stays informed and quality objectives are met, despite the physical distance. Building strong communication habits and leveraging collaboration tools are key to overcoming common remote work challenges in this role.

What are QA Architects?

QA Architects are experienced professionals who design and oversee the quality assurance processes within software development. They create overall testing strategies, select appropriate tools, and ensure that testing frameworks align with business goals and technical requirements. In a remote capacity, QA Architects collaborate with distributed teams, leveraging communication and project management tools to maintain consistent quality standards across projects.

What is the difference between Qa Architect Remote vs Qa Engineer Remote?

AspectQa Architect RemoteQa Engineer Remote
CredentialsTypically requires advanced certifications like ISTQB, CSQA, or equivalent, along with several years of experienceUsually requires foundational certifications such as ISTQB Foundation, with 1-3 years of experience
Work EnvironmentStrategic role involving architecture design, process improvement, and high-level planningExecution-focused role involving test case development, execution, and defect reporting
Industry UsageCommonly used in organizations with complex systems needing test architectureWidely used across various industries for day-to-day testing activities

The main difference between Qa Architect Remote and Qa Engineer Remote lies in their responsibilities and experience levels. Qa Architects focus on designing testing frameworks and strategies, requiring more experience and advanced certifications. Qa Engineers handle the execution of tests and defect management, often with less experience. Both roles are essential in quality assurance but serve different functions within the testing lifecycle.

What are the key skills and qualifications needed to thrive as a QA Architect in a remote setting, and why are they important?

To thrive as a QA Architect in a remote role, you need deep expertise in software testing methodologies, automation frameworks, and quality assurance principles, usually backed by a degree in computer science or related fields. Familiarity with tools like Selenium, JMeter, Jenkins, and CI/CD systems, as well as relevant certifications (e.g., ISTQB), is commonly expected. Strong analytical thinking, problem-solving abilities, and excellent communication skills help you collaborate effectively with distributed teams and lead quality initiatives. These skills are crucial for ensuring robust, scalable testing strategies and maintaining high software quality in complex, remote development environments.
More about Qa Architect Remote jobs
What cities are hiring for Qa Architect Remote jobs? Cities with the most Qa Architect Remote job openings:
What are the most commonly searched types of Qa Architect jobs? The most popular types of Qa Architect jobs are:
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Qa Architect Remote jobs near you
Infographic showing various Qa Architect Remote job openings in the United States as of June 2026, with employment types broken down into 2% Locum Tenens, 93% Full Time, 1% Part Time, and 4% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $171,297 per year, or $82.4 per hour.
Quality Assurance Engineer

Quality Assurance Engineer

Anika Systems

Leesburg, VA • On-site, Remote

Full-time

Posted 17 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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