JOB ID : QT-SNB-07-336

Job Summary

We are looking for a talented QA Engineer - Data Quality & Test Automation to join our Data Services team. The ideal candidate will have strong experience in data quality validation, test automation, Python, SQL, data reconciliation, and data workflow testing.

This role requires a strong QA mindset with the ability to design comprehensive test strategies, create automated test suites, validate large datasets, identify data-quality issues, and ensure that data products and pipelines meet business requirements.

The successful candidate will be a strong analytical thinker who can work closely with Data Engineers, Data Scientists, Analysts, and business stakeholders to improve solution quality and testability.

Key Responsibilities

- Develop and implement test strategies, test plans, and QA engineering practices for data-intensive applications and data products.
- Design and execute both manual and automated testing, with a strong focus on data quality and validation.
- Build robust automated test suites, test scripts, use cases, and validation frameworks.
- Develop test scenarios capable of identifying data defects, transformation issues, pipeline failures, and business-rule inconsistencies.
- Perform comprehensive data validation using SQL and Python.
- Validate data transformations, ETL/ELT workflows, data pipelines, and downstream datasets.
- Perform source-to-target data reconciliation and investigate data mismatches.
- Generate, manage, and maintain test datasets required for functional, regression, integration, and data-quality testing.
- Configure and maintain appropriate test environments and test configurations.
- Test data workflows involving large datasets and identify performance or scalability-related issues.
- Validate Power BI reports and dashboards, including data accuracy, calculations, filters, aggregations, and business rules.
- Create and maintain automated data-quality checks and validation frameworks.
- Define and track quality metrics for data pipelines, data products, and customer deliverables.
- Identify defects, document them accurately, assign appropriate severity/priority, and track them through successful resolution.
- Perform root-cause analysis of data-quality issues and collaborate with engineering teams on corrective actions.
- Work closely with Data Engineers, Data Scientists, and Data Analysts to ensure solutions are designed with testability and quality in mind.




- Participate in requirements and solution-design discussions and identify potential quality risks early in the development lifecycle.
- Conduct regression, integration, functional, system, and end-to-end testing.
- Research and evaluate current QA technologies, automation frameworks, and data-quality tools.
- Continuously improve test coverage, automation, quality processes, and engineering practices.

Required Technical Skills Programming & Automation
- Strong Python programming and scripting skills.
- Experience developing automated test frameworks and scripts.
- Proven experience with programmatic testing of software/data products.
- Strong understanding of automation concepts, reusable test components, and regression testing.

SQL & Data Validation
- Strong SQL fundamentals.
- Ability to write complex SQL queries for data validation and reconciliation.
- Experience validating:
- Data transformations
- Aggregations
- Joins
- Business rules
- Source-to-target mappings
- Data completeness
- Data accuracy
- Data consistency
- Duplicate records
- Null/invalid values

Data Quality & Data Testing
- Strong understanding of data quality principles.
- Experience with data reconciliation and data workflows.
- Experience testing ETL/ELT pipelines and data processing workflows.
- Ability to work with and validate large datasets.
- Experience identifying data anomalies and performing root-cause analysis.
- Ability to create meaningful data-quality metrics and validation rules.

Reporting & Tools
- Experience testing Power BI reports and dashboards.
- Experience with Jira or other defect/issue tracking systems.
- Familiarity with Git and software development workflows is preferred.
- Exposure to CI/CD-based automated testing is an advantage.

Key Testing Responsibilities

The candidate should be comfortable performing:

- Functional Testing
- Regression Testing
- Integration Testing
- System Testing
- End-to-End Testing
- Data Validation Testing
- Data Reconciliation
- ETL/ELT Testing
- API/Data Workflow Testing
- Automated Testing
- Data Quality Testing
- Power BI Report Testing
- Negative Testing
- Boundary and Edge-Case Testing
- Performance/Volume Testing

Qualifications
- 3+ years of experience in Quality Assurance,



with at least 2+ years in test automation and data validation.
- Strong hands-on experience with Python and SQL.
- Experience testing data workflows and performing data reconciliation.
- Experience developing automated test scripts/frameworks.
- Experience testing Power BI reports and dashboards.
- Proven ability to programmatically test software and data products.
- Experience working with defect tracking tools such as Jira.
- Exposure to large datasets and data-intensive applications is strongly preferred.
- Strong analytical, critical-thinking, and problem-solving skills.
- Excellent collaboration and communication skills.

Collaboration & Delivery
- Work closely with Data Engineering, Data Science, Analytics, and Product teams.
- Provide QA inputs during requirements and solution-design phases.
- Identify potential quality and testability risks before implementation.
- Communicate defects and technical findings clearly and constructively.
- Participate in Agile ceremonies and contribute to continuous improvement.
- Ensure testing activities align with business requirements and customer expectations.

Ideal Candidate Qualities
- Naturally curious and committed to understanding the why behind a requirement.
- Highly detail-oriented and passionate about finding defects and data inconsistencies.
- Focused on identifying root causes rather than surface-level symptoms.
- Strong ownership of quality from requirement analysis through production delivery.
- Customer-focused and empathetic toward business and marketing users.
- Enthusiastic about product success and continuously looks for ways to improve quality.
- Comfortable challenging assumptions and raising quality concerns constructively.
- Excellent communicator who can collaborate effectively with developers and data engineers.
- Passionate about automation, experimentation, and continuous learning.

Key Skills for Sourcing

QA Engineer | Data QA | Data Quality | Data Validation | Test Automation | Python | SQL | Data Reconciliation | ETL Testing | ELT Testing | Data Pipeline Testing | Data Workflow Testing | Large Datasets | Power BI Testing | Automated Testing | Regression Testing | Functional Testing | Integration Testing | Jira | Data Quality Metrics | Root Cause Analysis | API Testing

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 QA Engineer - Data Quality & Test Automation (Nagpur)
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