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Google 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 ...

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 ...

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 ...

Xometry

Quality Assurance Engineer

Gaithersburg, MD · On-site

$84K - $109K/yr

We are hiring a Quality Assurance Engineer to support our rapid customer growth. Do you thrive on ... Familiar with Google Workspace, Looker, Salesforce * Knowledge of Process Certification/Process ...

Jobgether

We are looking for a QA Engineer to help strengthen software quality across web and mobile applications within a fast-growing technology environment. This role focuses on embedding reliable testing ...

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 ...

FCS Financial

Quality Assurance Engineer

Jefferson City, MO · On-site

$87K - $151K/yr

Quality Assurance Engineer Quality Assurance Engineer Why FCS Financial? Ever dreamed of being part of an organization with deep roots in agriculture and the community? For over 100 years, as part of ...

Hired Recruiters

Senior QA Engineer

Los Altos, CA · On-site

Our AI-powered collaborative note-taking app works across Zoom, Google Meet, and Microsoft Teams to ... As the Senior QA Engineer , you will * Support testing of multiple features and drive the quality ...

TriOptus LLC

QA Engineer

Montpelier, VT · On-site

QA Engineer Job Title QA Automation Engineer (Web & GIS Applications) Join a State Government modernization project to ensure the quality, security, accessibility, and reliability of enterprise GIS ...

Moog, Inc.

Quality Assurance Engineer

Buffalo, NY · On-site

$65K - $90K/yr

Quality Assurance Engineer Reporting To: Manager, QA Engrg. Work Schedule: Onsite - Buffalo, NY Moog Military Aircraft Group is looking for a Quality Assurance Engineer to plan, implement, and ...

Tata Consultancy Services

QA Engineer

Atlanta, GA · On-site

Tata Consultancy Services is a leading IT services and consulting company, and they are seeking a QA Engineer. The role involves ensuring quality through various testing methodologies and tools, as ...

Cinder LLC

QA Engineer

Sunnyvale, CA · On-site

$60/hr

Title: QA Engineer Pay Rate: $60/hr Location: Sunnyvale, CA Type: Fulltime with benefits About Novus Labs Novus Labs is our sibling company that provides engineering support to the largest tech ...

Credigy

Quality Assurance Engineer

Norcross, GA · On-site

Quality Assurance Engineer, Product Strategy Quality doesn't happen by accident - it takes someone who is methodical, curious, and genuinely invested in getting it right. Credigy's Product Strategy ...

Membersuite

Quality Assurance Engineer

Dunwoody, GA

We are looking for an extremely bright, experienced QA engineer that is self-driven and able to investigate products, environments and associated systems independently. Our product is extremely ...

corpay

QA Engineer

Beaverton, OR

What We Need Corpay is currently looking to hire a QA Engineer within our Corpay Payables division. This position falls under our Corporate Payments line of business and is located in Beaverton, OR.

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Google Qa Engineer information

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How much do google qa engineer jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for google 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.

How much does a QA engineer make at Google?

A QA engineer at Google typically earns a base salary ranging from $80,000 to $150,000 annually, depending on experience, location, and level. Compensation may also include bonuses, stock options, and benefits, with more experienced engineers earning higher total compensation.

What engineer makes $500,000 a year?

Senior engineers at top tech companies like Google, especially those in specialized roles such as Google QA Engineers with extensive experience, can earn $500,000 or more annually through base salary, bonuses, and stock options. Achieving this level typically requires advanced skills, years of experience, and often leadership responsibilities or highly specialized expertise.

What kind of projects and challenges can a QA Engineer expect to work on at Google?

As a Google QA Engineer, you will work on large-scale, cutting-edge projects that require thorough testing of web applications, mobile apps, and cloud-based services. You’ll frequently face challenges such as designing robust automated test suites, identifying edge cases, and collaborating closely with developers to resolve complex issues quickly. Your day-to-day tasks may include writing and maintaining test scripts, analyzing test results, and ensuring comprehensive coverage for new and existing features. The environment is highly collaborative, and you’ll have opportunities to participate in code reviews, influence product quality across teams, and contribute to continuous improvement of testing processes.

What is the salary of QA tester in Google?

The salary of a QA tester at Google typically ranges from $70,000 to $120,000 annually, depending on experience, location, and specific role level. Entry-level positions may start lower, while experienced testers or those in senior roles can earn higher salaries, often supplemented with benefits and bonuses.

What are the key skills and qualifications needed to thrive in the Google Qa Engineer position, and why are they important?

To thrive as a Google QA Engineer, you need a solid background in software testing, knowledge of programming languages such as Java, Python, or C++, and a strong understanding of software development life cycles. Familiarity with automation frameworks like Selenium or Appium, experience with bug-tracking systems like JIRA, and relevant certifications in quality assurance or software testing are often expected. Excellent analytical skills, attention to detail, and effective collaboration and communication abilities help you stand out in this role. These competencies are essential for ensuring Google’s products meet high performance and reliability standards in dynamic, fast-paced engineering environments.

Is QA Engineer still in demand?

Yes, QA Engineer roles remain in demand as companies prioritize software quality assurance, especially with the growth of automation tools like Selenium and testing frameworks. Skilled QA engineers with knowledge of scripting, testing methodologies, and tools are sought after across various industries to ensure product reliability and performance.

What is a Google QA Engineer job?

A Google QA Engineer ensures the quality and reliability of software products by designing and executing test plans, identifying bugs, and improving automation frameworks. They collaborate with developers to enhance testing efficiency and maintain high engineering standards. Their work involves manual and automated testing, performance analysis, and reporting defects to ensure a seamless user experience.

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What cities are hiring for Google Qa Engineer jobs? Cities with the most Google Qa Engineer job openings:
What are the most commonly searched types of Google Qa Engineer jobs? The most popular types of Google Qa Engineer jobs are:
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Google Qa Engineer jobs near you
Infographic showing various Google Qa Engineer job openings in the United States as of July 2026, with employment types broken down into 86% Full Time, and 14% Contract. Highlights an 100% In-person 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 24 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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