Data QA Engineer

Jobgether; United States; Remote; USD 90,000 - 150,000; Full time

Benefits offered by this job

35 days of paid holidays

Flexible, location-independent working

Global, multicultural team

Open-source technology focus

Autonomy and ownership

Collaborative environment

Job summary

Jobgether, a partner company, is seeking a Data QA Engineer based in the United States. You will ensure data accuracy, completeness, and reliability for web data extraction and AI-assisted solutions, translating customer requirements into measurable QA standards.

You will blend exploratory testing, data analysis, and automation, using Python and SQL to validate datasets, report findings, and help improve testing processes across a global, distributed team.

Qualifications

• 5+ years in QA, software/data testing or data quality assurance.
• Proficient in Python for data analysis and validation (Pandas).
• SQL skills to query and validate data in relational DB.
• Familiar with HTML, XPath, JSON, HTTP, CSS and web tech.
• Experience testing HTTP/REST APIs and validating responses.
• Knowledge of Agile (Scrum/Kanban) environments.
• Understanding of data quality: completeness, accuracy, drift.
• Strong English written and spoken communication.

Responsibilities

• Translate customer data extraction requirements into clear QA criteria.
• Perform exploratory, manual, and automated testing where valuable.
• Execute test cycles on datasets to validate against specs.
• Document defects with reproducible steps and evidence.
• Apply statistics and visualizations to assess data completeness and drift.
• Prepare quality reports and scoring metrics for teams and customers.
• Define testing scope, untested areas, and associated risks.
• Collaborate with PMs and customers to clarify requirements.
• Contribute to test automation framework improvements.
• Propose QA process/tooling improvements, including UI testing tools.

Skills

Python for data analysis

SQL basics

Data quality principles

Agile collaboration

English communication

Tools

Pandas

Jupyter notebooks

HTTP APIs testing

Scrapy (nice to have)

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data QA Engineer based in United States.

As a Data QA Engineer, you will play a key role in ensuring the accuracy, completeness, and reliability of data delivered to customers.

You will work with web data extraction projects and AI-assisted solutions, translating customer requirements into clear, measurable quality standards.

Rather than treating quality assurance as a final checkpoint, you will help identify potential issues early and prevent defects before they reach production.

Combining exploratory testing, data analysis, and automation, you will investigate anomalies and provide actionable insights to developers and project teams.

You will use Python, SQL, and modern testing tools to assess data quality and communicate results through meaningful reports, metrics, and visualizations.

Working closely with project managers, developers, and clients, you will have the autonomy to improve testing processes and strengthen quality practices.

This is an opportunity to join a globally distributed, multicultural team that values ownership, continuous improvement, and flexible ways of working.

Accountabilities

• Translate customer data extraction and web scraping requirements into clear, testable criteria and comprehensive QA strategies, taking ownership of quality assurance from the beginning of new projects.
• Perform exploratory, manual, and visual testing, complemented by automated testing where it provides measurable value.
• Execute test cycles on extracted datasets to validate compliance with customer specifications and identify inconsistencies, inaccuracies, missing information, and other defects.
• Document defects clearly and thoroughly, providing reproducible steps and supporting evidence so developers can investigate and resolve issues efficiently.
• Apply descriptive statistics, data summaries, and visualizations to assess completeness, accuracy, consistency, and data drift.
• Prepare quality reports and scoring metrics that communicate results honestly to internal teams and, where appropriate, customers.
• Clearly identify the scope of testing, any untested areas, and the associated risks to support informed project decisions.
• Collaborate with project managers and customers to clarify ambiguous requirements and ensure that expectations are measurable and testable before QA begins.
• Contribute to the development and improvement of test automation frameworks, with an emphasis on coverage, reliability, and performance.
• Identify, propose, and implement improvements to QA processes and tooling, including browser extensions, spreadsheet add-ons, and user-interface-based testing tools.
• Work independently while maintaining close collaboration with QA leadership, developers, and project managers to uphold quality standards throughout delivery.

Requirements

• At least 5 years of experience as a QA Engineer or in a comparable role involving software testing, data testing, or data quality assurance.
• Proficiency in Python for data analysis and validation, preferably using Pandas and Jupyter notebooks.
• Basic to intermediate SQL skills, including the ability to query and validate data in at least one relational database environment.
• Strong understanding of web technologies and protocols, including HTML, XPath, JSON, HTTP, and CSS.
• Experience testing HTTP/REST APIs and validating their responses against expected requirements.
• Solid knowledge of test case design, test data management, and practical approaches to exploratory and automated testing.
• Understanding of data quality principles, particularly completeness, accuracy, consistency, and data drift.
• Experience working in an Agile environment, such as Scrum or Kanban, and collaborating effectively with developers.
• Familiarity with AI and large language model (LLM) tools to improve test case generation and accelerate data validation workflows.
• A proactive, detail-oriented mindset with strong ownership of project quality and the confidence to uphold standards under delivery pressure.
• Ability to learn new technologies quickly, understand complex project requirements, and adapt testing strategies to different domains.
• Strong written and spoken English communication skills, with the ability to explain technical findings clearly and precisely.

Nice To Have

• Practical knowledge of Scrapy or other Python-based web scraping frameworks.
• Experience working with test automation frameworks.
• An interest in data science concepts and their application to data analysis and validation.
• Enthusiasm for exploring AI and LLM-powered approaches to improve data quality assurance.

Benefits

• 35 days of paid holidays, supporting substantial time away from work.
• Flexible, location-independent working, with the freedom to work from where you are most productive.
• Global, multicultural team: Join a self-motivated, progressive, and internationally distributed team.
• Modern technology: Work with cutting-edge open-source technologies and tools.
• Autonomy and ownership: Contribute ideas, take responsibility for improvements, and help shape QA processes and tooling.
• Collaborative environment: Work closely with technical and project teams to solve meaningful data quality challenges.
• Additional details regarding salary, healthcare, retirement plans, and other benefits were not specified in the original job description.

Data Privacy Notice:

By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.