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Workday Qa Engineer Jobs (NOW HIRING)

Anika Systems

Quality Assurance Engineer

Leesburg, VA · On-site +1

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 ...

Hexagon AB

Quality Assurance Engineer

Houston, TX · On-site

Quality Assurance Engineer Job Location (Short): Houston, Texas-USA | Madison, Alabama-USA Workplace Type: Remote Req Id: 2762 Responsibilities Octave is seeking a Senior Quality Assurance Engineer ...

Vertex Sigma Software

QA Engineer

Foster City, CA

QA Engineer Onsite in Foster City, CA | 5 days in office The Quality Assurance team is actively seeking a Quality Assurance Engineer to help ensure that our backend services are optimized, secure ...

THRONE LABS INC

QA Engineer

Washington, DC · Remote

Software Quality Assurance Engineer FULL-TIME, WASHINGTON, D.C. About the job Throne aims to provide peace of mind to those that venture out each day -- making it so no one has to worry about finding ...

THRONE LABS INC

QA Engineer

Los Angeles, CA · Remote

Software Quality Assurance Engineer FULL-TIME, WASHINGTON, D.C. About the job Throne aims to provide peace of mind to those that venture out each day -- making it so no one has to worry about finding ...

BWX Technologies

QA Engineer

Barberton, OH

Quality Assurance Engineer - BWX Technologies, Inc. - Barberton, OH Responsibilities include: * Support of production manufacturing processes. * Resolution of quality issues. * Assist in the ...

THRONE LABS INC

QA Engineer

Los Angeles, CA · Remote

Software Quality Assurance Engineer FULL-TIME, WASHINGTON, D.C. About the job Throne aims to provide peace of mind to those that venture out each day -- making it so no one has to worry about finding ...

Redolent, Inc

QA Engineer

Sunnyvale, CA

Hello, One of our direct client is urgently looking forQA Engineer @ Sunnyvale , CA TITLE: QA Engineer LOCATION: Sunnyvale , CA Duration: 6 to 12+ months Description QE Engineer in this role will ...

Dexian

QA Engineer

Columbia, MD · On-site

Job Summary The Quality Assurance (QA) Engineer is responsible for ensuring that software releases meet defined quality standards through thorough and accurate testing. As a member of the engineering ...

CivicMinds, Inc.

Quality Assurance Engineer

Belmont, NH · On-site

Quality Assurance Engineer Location: Belmont, NH Type: Fulltime, On-site Summary: The company's products include DC/DC converters, AC/DC inverters, solid-state power distribution, generator control ...

HP IQ

Quality Assurance Engineer

San Francisco, CA

$85K - $130K/yr

We are looking for a Quality Assurance Engineer to lead test strategy and quality initiatives across software and hardware teams. You'll define and scale test automation, influence architecture for ...

BWX Technologies

QA Engineer

Barberton, OH

Quality Assurance Engineer - BWX Technologies, Inc. - Barberton, OH Responsibilities include: * Support of production manufacturing processes. * Resolution of quality issues. * Assist in the ...

THRONE LABS INC

QA Engineer

Washington, DC · Remote

Software Quality Assurance Engineer FULL-TIME, WASHINGTON, D.C. About the job Throne aims to provide peace of mind to those that venture out each day -- making it so no one has to worry about finding ...

MM International

QA Engineer

Redmond, WA · On-site

QA Engineer - Onsite (Redmond, WA) We're hiring a QA Engineer to support Windows security initiatives. This is an onsite role in Redmond , with a strong preference for local candidates (relocation ...

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Workday Qa Engineer information

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How much do workday qa engineer jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for workday qa engineer in the United States is $48.54, according to ZipRecruiter salary data. Most workers in this role earn between $38.22 and $55.53 per hour, depending on experience, location, and employer.

What are some common challenges faced by Workday QA Engineers when testing integrations and updates?

Workday QA Engineers often face challenges related to the complexity and frequency of Workday updates, as well as the need to validate integrations with other enterprise systems. Testing new releases requires a deep understanding of both configuration and business processes to ensure that updates do not disrupt existing workflows. Additionally, collaborating with functional analysts, developers, and HR teams is crucial to identify test scenarios and resolve issues quickly. Staying current with Workday’s evolving features and maintaining robust test scripts helps mitigate risks and ensures smooth deployments.

What is a Workday QA Engineer?

A Workday QA Engineer is a quality assurance professional who specializes in testing and validating Workday, a popular cloud-based Human Capital Management (HCM) and financial management software. Their primary role is to ensure that Workday implementations, integrations, and updates function correctly and meet business requirements. This includes creating and executing test plans, identifying bugs or issues, collaborating with developers and stakeholders, and maintaining documentation. Workday QA Engineers often use both manual and automated testing methods to verify the software's performance and reliability. Their expertise helps organizations maintain smooth HR and financial operations within the Workday platform.

What is the difference between Workday Qa Engineer vs Workday Functional Consultant?

AspectWorkday Qa EngineerWorkday Functional Consultant
Primary RoleFocuses on testing and quality assurance of Workday applicationsImplements and configures Workday modules based on client requirements
Required SkillsTesting, QA processes, Workday knowledge, scriptingBusiness process configuration, requirements gathering, Workday expertise
CertificationsWorkday Certification, QA/testing certificationsWorkday Certification, Business process certifications
Work EnvironmentTesting teams, QA departments, IT projectsConsulting firms, client sites, HR/Finance departments

While both roles require Workday knowledge and certifications, the Workday Qa Engineer primarily focuses on testing and ensuring the quality of Workday applications, whereas the Workday Functional Consultant is responsible for implementing and configuring Workday modules to meet client needs.

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

To thrive as a Workday QA Engineer, you need expertise in software testing methodologies, a solid understanding of Workday HCM or Finance modules, and experience with test automation, often supported by a relevant degree in computer science or IT. Familiarity with QA tools like Selenium, Jira, and Workday's own testing frameworks, along with certifications such as Workday Pro or ISTQB, is highly valuable. Strong analytical thinking, problem-solving abilities, and effective communication skills help you collaborate with cross-functional teams and troubleshoot complex issues. These skills ensure accurate, efficient testing and high-quality Workday system implementations that meet business requirements.
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Workday Qa Engineer jobs near you
Infographic showing various Workday Qa Engineer job openings in the United States as of May 2026, with employment types broken down into 99% Full Time, and 1% Part Time. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $100,970 per year, or $48.5 per hour.
Quality Assurance Engineer

Quality Assurance Engineer

Anika Systems

Leesburg, VA • On-site, Remote

Full-time

Posted 16 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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