Job Description - Senior QA Data Engineer

Key Responsibilities:

- Testing Strategy: Plan, design, and execute comprehensive testing strategies for new features, enhancements, and system changes.
- Hands-on Testing: Create, maintain, and execute manual and automated test cases ensuring thorough coverage.
- Automation Leadership: Champion automation best practices by designing scalable, maintainable solutions supporting continuous delivery.
- Functional & Non-functional Testing: Validate system quality, performance, and reliability across the SDLC.
- Defect Management: Identify, analyse, and troubleshoot defects with root cause analysis, collaborating with cross-functional teams for resolution.
- Reporting: Produce explicit, detailed defect reports to support efficient remediation.
- Stakeholder Communication: Share progress, risks, and quality insights with technical and non-technical stakeholders.

Knowledge & Experience Required:

Data Quality & Validation (Azure Data Platforms)

- ADF & Databricks Validation: Ensure accuracy, reliability, and consistency across complex workflows.
- ETL/ELT Testing: Validate ingestion, transformations, schema, and reconciliation between source and target systems.
- Data Profiling: Identify anomalies, duplicates, and integrity issues.
- Lineage & Monitoring: Validate lineage, auditing, and monitoring using observability tools.

Test Automation for Data Platforms

- Automation Frameworks: Design frameworks using Python (PyTest, PySpark, Pandas) or C#.
- Reusable Scripts: Build scripts for pipelines, transformations, and integrations.
- SQL Integration Tests: Develop integration tests for queries and workflows.

SQL & Database Testing

- Advanced SQL: Validate business rules, transformations, and workflows.
- Source-to-Target Validation:



Ensure accuracy post-transformations.
- Database Structures: Validate indexes, constraints, stored procedures, and performance.

Business Intelligence & Reporting Testing

- BI Tools: Validate dashboards, reports, datasets, and models.
- Power BI: Work with DAX queries and tools like DAX Studio.
- Power Platform: Test Power Apps and Dataverse solutions.

CI/CD & Data Pipeline Testing

- Pipeline Integration: Embed validation tests into Azure DevOps/GitHub Actions.
- Deployment Validation: Support deployment validation for ETL pipelines and BI models.

Data Warehouse & Traditional ETL Tools

- Warehouse Testing: Validate architectures, data modelling, and enterprise platforms.
- ETL Tools: Experience with SAP BO Data Services, Informatica, SSIS.

Quality Engineering & Collaboration

- Enterprise QA: Strong focus on automation and quality engineering practices.
- Agile Practices: Active contribution to sprint ceremonies and Three Amigos sessions.
- Mentorship: Mentor QA engineers, conduct code reviews, and promote best practices.
- Collaboration: Partner with BAs and Developers to refine requirements and acceptance criteria.

Essential Skills:

- Cloud & Data Platforms: Azure Data Factory, Databricks, Synapse, SQL.
- Databases & Storage: SQL Server, Delta Lake.
- Programming & Scripting: Python (PySpark, Pandas), SQL, C#, REST APIs, DAX.
- Automation Tools: PyTest, Great Expectations, DataDiffPy, Postman.
- CI/CD & DevOps: Azure DevOps, GitHub Actions.
- Monitoring: Azure Monitor, Log Analytics, Databricks Monitoring.
- BI & Power Platform: Power BI, Power Apps, Dataverse.
- Database Testing: Advanced SQL for validation and performance analysis.
- Collaboration & Soft Skills: Strong teamwork, communication, and Agile collaboration.

📌 Senior Data Engineer QA (Mumbai)
🏢 Atyeti
📍 Mumbai