Senior QA Analyst

Senior Data Engineer

📍 Remote – Must have 4+ hours overlap with PST
🕒 Full-Time | Contractor

About the Client

Our client is a fast-growing US-based digital company specializing in performance-driven growth strategies for online brands. They leverage data, technology, and automation to deliver scalable solutions across various industries. With a fully remote team and a results-focused culture, they are committed to innovation, collaboration, and operational excellence.

About the Role

We are looking for a meticulous and experienced Senior QA Analyst to ensure the accuracy, completeness, and reliability of data across multiple systems and platforms. You will play a key role in identifying discrepancies, validating ETL processes, and ensuring that critical business data meets quality standards.

You’ll collaborate closely with data engineers, analysts, and stakeholders to test and validate data flows, document findings, and continuously improve data quality frameworks. This is an ideal role for someone who is highly analytical, detail-oriented, and thrives in a fast-paced, data-centric environment.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, or related technical field.
• Minimum 5+ years of experience in data QA, data analysis, or similar roles.
• Strong expertise in SQL and working with large data sets.
• Experience with ETL testing and validating data pipelines and warehouse systems.
• Familiarity with data QA tools (e.g., QuerySurge, Informatica DVO, Talend).
• Solid understanding of data profiling, cleansing, and validation techniques.
• Experience with automated testing tools (e.g., Selenium, Python) is a plus.
• Strong analytical skills and keen attention to detail.
• Ability to work independently and in collaborative, cross-functional teams.
  
   

Preferred Skills

• Experience with cloud platforms: AWS, Azure, or Google Cloud
• Familiarity with tools like DBT, Snowflake, Airflow, Airbyte, AWS RDS, Power BI, Google BigQuery, and Looker
• Understanding of data governance and data management principles
  
   

Work Schedule

• Must have a minimum of 4 hours overlap with US Pacific Time (PST) between 6:00 AM – 10:00 AM PST, Monday to Friday

• Data Validation & Testing:
  • Design and execute test plans, test cases, and test scripts to validate data accuracy, completeness, consistency, and integrity.
  • Verify data correctness across different systems and applications, including databases, data lakes, data warehouses, and cloud environments.
  • Conduct manual and automated data testing, comparing expected results with actual results.
• ETL & Data Pipeline Testing:
  • Test data pipelines and ETL processes to ensure proper data transformation, load, and storage.
  • Validate data mapping and transformation rules based on business requirements.
• SQL Querying & Data Analysis:
  • Write complex SQL queries to extract and analyze data for testing purposes.
  • Compare source and target data sets to verify the accuracy and consistency of data migration and integration processes.
• Data Quality Assurance:
  • Collaborate with data engineers, business analysts, and developers to understand business requirements and ensure proper data quality controls are implemented.
  • Identify, report, and track data-related issues or anomalies in the system and collaborate to resolve them.
  • Develop and maintain data quality reports and dashboards to track data integrity issues.
• Automation & Tools:
  • Leverage data testing tools and automation frameworks to improve the efficiency and effectiveness of data quality testing.
  • Implement automated test scripts to perform regression testing for data changes.
• Documentation & Reporting:
  • Document data validation processes, test results, and issue logs in a clear and structured manner.
  • Provide regular reports on data quality metrics, test outcomes, and potential risks to stakeholders.