Senior QA Engineer: 6–8 Years - 2 positions
QA Engineer (Mid-Level): 3–5 Years - 2 positions
Job Function: Digital Assurance / Quality Engineering
Employment Type: Full-time
Job Summary
Blumetra Solutions is seeking Application and Data Validation Engineers with strong expertise in data validation, SQL, ETL/ELT testing, API testing, functional testing and end-to-end system integration testing.
The successful candidates will be responsible for validating data pipelines, integrations, data models, analytics solutions & Applications while developing automated test scripts and reusable validation utilities. Experience with Python, Playwright, Snowflake/ Databricks, AWS or Azure, and AI-assisted development tools such as Claude is highly desirable.
Candidates should demonstrate strong ownership, analytical skills, attention to detail, and the ability to translate business requirements into comprehensive test scenarios and data quality checks.
Key Responsibilities
- Data Validation & Testing: Validate data integrations, ETL/ELT pipelines, data models, Medallion Architecture (Bronze, Silver, and Gold layers), semantic models, dashboards, and visualizations.
- SQL Testing: Write and execute SQL queries to verify data accuracy, completeness, consistency, integrity, transformation logic, and source-to-target reconciliation.
- API & Integration Testing: Perform API testing, end-to-end testing, and system integration testing across applications, services, and data platforms.
- Test Automation: Develop and maintain automated test scripts, frameworks, and reusable validation utilities using Python, Playwright, API automation tools, and other relevant technologies.
- Data Lineage & Metadata Validation: Validate data lineage, metadata, transformation rules, and data movement across different systems and architectural layers.
- AI-Assisted Test Development: Use Claude or other AI-powered tools to develop, enhance, and maintain data validation utilities, automation scripts, and QA accelerators. Support automation utility/ scripts maintenance, version control, and deployment.
- Cloud & Data Platform Testing: Test data pipelines and integrations involving AWS or Microsoft Azure and Snowflake/ Databricks.
- Functional Testing: Understand business requirements and functional workflows to design relevant test scenarios and validate expected business outcomes.
- Test Documentation: Prepare and maintain test plans, test cases, requirements traceability matrices (RTMs), test execution reports, defect reports, and test summary reports. Leverage AI for documentation as much as possible.
- Defect Management: Identify, document, track, and retest defects. Participate in defect triage and root-cause analysis with development and data engineering teams.
- Quality Checklists: Execute QA entry and exit checklists, support stage-gate reviews, and ensure testing deliverables meet defined acceptance criteria.
- Stakeholder Collaboration: Coordinate with developers, data engineers, business analysts, project managers, and customer teams to resolve issues and ensure quality deliverables with an intent of ownership to close the open items.
- Agile Delivery: Participate in sprint planning, daily stand-ups, sprint reviews, retrospectives, and other Agile/Scrum activities using Jira. Ensure Story/ task detailing with right level of acceptance criteria, calling out dependencies and risks upfront.
Required Skills & Experience
- 3–8 years of relevant experience in QA, data testing, data validation, or quality engineering, depending on the position.
- Strong SQL skills and experience validating data across source and target systems.
- Hands-on experience with ETL/ELT testing, data integration, data reconciliation, and data quality assurance.
- Understanding of data modelling, Medallion Architecture, semantic models, and visualization testing.
- Experience in functional, API testing and end-to-end system integration testing.
- Hands-on experience with Python-based automation, Playwright, API automation, or comparable testing frameworks.
- Understanding of metadata, data lineage, transformation logic, and cross-system data flows.
- Experience using Claude or equivalent AI tools for automation development and code assistance.
- Working experience with Snowflake/ Databricks and at least one cloud platform: AWS or Microsoft Azure.
- Strong analytical, troubleshooting, documentation, and communication skills.
- Familiarity with Jira and Agile/Scrum methodologies.
Experience-Based Expectations
QA Engineer – Mid-Level (3–5 Years)
- Execute data validation, SQL testing, functional, API testing, and integration testing activities.
- Develop and maintain automation scripts and reusable test utilities.
- Prepare test cases, traceability documentation, execution reports, and defect records.
- Investigate data discrepancies and collaborate with engineering teams to resolve issues.
- Follow established test strategies, quality checklists, and reporting standards.
Senior QA Engineer (6–8 Years)
- Own end-to-end testing activities for complex data integrations and pipelines.
- Design comprehensive test scenarios, validation strategies, and reusable automation components.
- Independently manage test execution, defect triage, quality reporting, and testing risks.
- Review test cases and automation scripts and mentor junior QA engineers where required.
- Coordinate with customer and cross-functional teams to resolve complex quality issues.
- Support QA stage gates, release readiness assessments, and continuous improvement initiatives.
Preferred Domain Experience
- Retail and Supply Chain domain experience is preferred.
- Life Sciences domain experience is an added advantage.
Ideal Candidate Profile
A detail-oriented hands-on QA professional who understands business problem, gains depth in domain/ functionality, has strong SQL and data validation skills, practical automation experience, understanding of modern cloud data platforms and hands-on with AI tools. The candidate should be comfortable working across data engineering and business teams to ensure accurate, consistent, and reliable data throughout the enterprise data lifecycle.