QA & Data Validation Engineer at Nexaxis Technology Solutions · Remote, Delhi, Gurugram, Noida, Ghaziabad, Faridabad · 5 - 8 years · ₹10L - ₹12L / yr · Profitable · Remote friendly · Posted 2 Sep 2026
PythonJob Description – QA & Data Validation Engineer
Experience: 5–6 Years
Location: Pan India
Employment Type: Full-Time
Work Mode: Pan India / Remote or Hybrid as applicable
About the Role
We are looking for an experienced QA & Data Validation Engineer with 5–6 years of hands-on experience in data quality assurance, solution analysis, data validation, SQL, Python, PySpark, Azure Data Factory, Azure Synapse Analytics, and Power BI validation.
The ideal candidate will be responsible for validating large-scale data pipelines, performing source-to-target reconciliation, analyzing business rules, investigating data defects, and ensuring the accuracy, completeness, and consistency of data across source, staging, intermediate, and target systems.
The role requires strong analytical and problem-solving skills along with the ability to work closely with development, data engineering, business, and other stakeholders in an Agile delivery environment.
You will also contribute to the design, development, and maintenance of automated validation frameworks and utilities using Python, SQL, PySpark, Azure Data Factory, and Azure Synapse.
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Key Responsibilities
1. QA & Solution Analysis
- Analyze business and technical requirements to understand data processing and validation needs.
- Participate in requirement analysis sessions and clarify functional and technical requirements with stakeholders.
- Review solution designs, data flows, mapping documents, interface specifications, and business rules.
- Validate that implemented solutions meet defined business and technical requirements.
- Identify gaps, inconsistencies, ambiguities, and potential data quality issues during requirement and solution analysis.
- Translate business requirements into detailed test scenarios, test cases, and validation conditions.
- Perform end-to-end validation of data processing workflows.
- Ensure data is accurately processed from source systems through intermediate layers to final outputs.
- Validate business rules and transformation logic implemented within data pipelines.
2. Test Planning & Execution
- Prepare comprehensive test strategies, test plans, test scenarios, and test cases for data-intensive applications.
- Execute functional, integration, regression, system, and data validation testing.
- Perform positive and negative testing for different data processing scenarios.
- Validate data pipelines across multiple environments, including staging, testing, and production.
- Identify test data requirements and prepare appropriate datasets for validation.
- Execute SQL queries to validate data processing and transformation results.
- Document test results, observations, defects, and validation evidence.
- Track testing progress and communicate status, risks, issues, and dependencies to stakeholders.
3. Data Validation & Reconciliation
- Perform detailed source-to-target data validation and reconciliation.
- Validate source, intermediate, staging, and output datasets.
- Perform record count validation between source and target systems.
- Verify data completeness, consistency, accuracy, and integrity.
- Validate data transformations against defined business rules.
- Perform field-level and record-level comparisons.
- Validate data types, formats, precision, scale, and null handling.
- Verify schema structure, layout, column names, and column sequence.
- Validate mandatory and optional fields.
- Identify missing, duplicate, truncated, or incorrectly transformed records.
- Analyze invalid records, rejected records, and exception datasets.
- Verify exception and reject-handling mechanisms.
- Compare production and staging data to identify discrepancies.
- Perform reconciliation between files, databases, and reporting layers.
- Validate data across different processing stages and identify the root cause of discrepancies.
4. File & Data Processing Validation
- Validate large-scale datasets across multiple file formats.
- Perform validation of:
- CSV files
- Delimited files
- Fixed-width files
- Excel files
- Database tables
- Structured and semi-structured datasets
- Validate file layouts, headers, delimiters, record formats, and column sequences.
- Verify file-level and record-level counts.
- Analyze source, intermediate, and final output files.
- Validate file-to-database and database-to-file reconciliation.
- Identify incomplete, corrupted, malformed, or invalid records.
- Verify data movement and transformation between different storage locations.
- Validate Azure-to-AWS file transfer processes.
- Ensure transferred files are complete and match the expected source datasets.
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5. Defect Investigation & Root Cause Analysis
- Investigate data discrepancies and application/data pipeline defects.
- Perform detailed root cause analysis for data quality and validation failures.
- Analyze source data, transformation logic, pipeline execution, database records, and output datasets to identify defects.
- Collaborate with developers and data engineers to resolve identified issues.
- Reproduce defects and provide detailed technical evidence.
- Perform defect impact analysis.
- Conduct retesting and regression testing after defect resolution.
- Monitor recurring data quality issues and recommend preventive solutions.
- Maintain detailed defect documentation and validation results.
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6. Python Development & Automation
- Develop Python scripts and utilities for data validation and reconciliation.
- Design, develop, and maintain reusable data validation frameworks.
- Automate repetitive data comparison and validation activities.
- Build automated utilities for:
- Record count validation
- Data completeness checks
- Schema validation
- Column sequence validation
- Source-to-target comparison
- Duplicate detection
- Exception identification
- Data quality checks
- Automated reporting
- Develop Python-based validation and reporting utilities.
- Optimize Python scripts for processing large datasets.
- Maintain and enhance existing automation frameworks.
- Implement reusable validation components to improve testing efficiency and coverage.
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7. SQL Development & Data Analysis
- Write complex SQL queries for data analysis and validation.
- Perform data extraction and comparison using SQL Server / SSMS.
- Validate source and target database records.
- Perform joins, aggregations, subqueries, CTEs, and analytical queries as required.
- Develop SQL queries to identify data mismatches, duplicates, missing records, and transformation issues.
- Validate database tables, schemas, columns, constraints, and relationships.
- Perform record count and reconciliation checks using SQL.
- Analyze SQL Server metrics databases.
- Validate data processing results against expected business rules.
- Troubleshoot data discrepancies using SQL queries.
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8. PySpark & Large-Scale Data Processing
- Develop and execute PySpark notebooks for large-scale dataset processing and validation.
- Analyze large volumes of structured and semi-structured data.
- Perform data transformation and validation using PySpark.
- Compare large source and target datasets efficiently.
- Implement data quality and reconciliation checks using PySpark.
- Analyze exception, reject, and invalid datasets.
- Optimize data validation processes for large datasets.
- Work with Azure Synapse notebooks and data processing environments.
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9. Azure Data Factory & Pipeline Testing
- Design and execute validation scenarios for Azure Data Factory (ADF) pipelines.
- Validate pipeline execution, data movement, transformations, and dependencies.
- Monitor pipeline runs and investigate failures.
- Validate source-to-target data movement through ADF.
- Develop and maintain test pipelines using Azure Data Factory.
- Verify pipeline parameters, triggers, activities, and execution results.
- Validate file ingestion and processing workflows.
- Perform end-to-end testing of data pipelines.
- Investigate pipeline-related data discrepancies and failures.
---
10. Azure Synapse Analytics
- Work with Azure Synapse Analytics for data validation and analysis.
- Develop and execute Synapse notebooks using PySpark.
- Validate datasets processed through Synapse pipelines and notebooks.
- Perform data quality and reconciliation checks within Synapse environments.
- Analyze large-scale datasets and processing results.
- Validate data movement between Azure storage, Synapse, databases, and reporting systems.
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11. Azure Storage & Cosmos DB
- Validate data stored in Azure Storage Accounts and Containers.
- Verify file ingestion, processing, and output data.
- Perform file-level and content-level validation within Azure storage.
- Validate data processing workflows involving Azure Storage.
- Perform data validation in Azure Cosmos DB.
- Verify records, fields, formats, and data completeness within Cosmos DB.
- Investigate discrepancies between source files, Azure storage, databases, and Cosmos DB.
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12. AWS S3 & Azure-to-AWS Validation
- Validate files stored in AWS S3.
- Perform source-to-target validation for files transferred between Azure and AWS.
- Verify file counts, file names, sizes, formats, and record counts.
- Compare source files with transferred S3 files.
- Validate data integrity after cloud-to-cloud file transfers.
- Investigate missing, incomplete, duplicate, or corrupted files.
- Support end-to-end validation of Azure-to-AWS data movement processes.
---
13. Metrics, Reporting & Power BI Validation
- Extract and validate source system metrics.
- Validate metrics stored in SQL Server databases.
- Perform reconciliation between source metrics, database metrics, and reporting outputs.
- Validate Power BI dashboards and reports against underlying source data.
- Verify report calculations, KPIs, measures, filters, and aggregations.
- Perform file-to-database-to-Power BI reconciliation.
- Validate data displayed in Power BI against SQL Server and source datasets.
- Identify discrepancies between backend data and dashboard results.
- Support reporting and analytics teams with data validation and troubleshooting.
---
14. Production Support & Job Monitoring
- Monitor scheduled data processing jobs and pipelines.
- Perform production validation and health checks.
- Analyze production failures and data discrepancies.
- Support incident investigation and resolution.
- Compare production and staging environments to identify differences.
- Validate production data after deployments and pipeline executions.
- Monitor ECG jobs and provide support for job execution and data processing issues.
- Perform post-production validation and reconciliation.
- Communicate critical production issues and risks to relevant stakeholders.
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15. Agile Delivery & Stakeholder Collaboration
- Work effectively within an Agile/Scrum delivery environment.
- Participate in sprint planning, daily stand-ups, backlog refinement, sprint reviews, and retrospectives.
- Collaborate with Business Analysts, Developers, Data Engineers, DevOps teams, Product Owners, and other stakeholders.
- Provide timely updates on testing progress and issues.
- Participate in requirement clarification and solution discussions.
- Support release planning and production deployment activities.
- Track work items and defects using Rally.
- Ensure testing activities are aligned with sprint and release timelines.
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Required Technical Skills
Mandatory Skills
- 5–6 years of experience in QA / Data Validation / Data Testing / Data Quality Engineering.
- Strong experience in SQL and data analysis.
- Hands-on experience with Python development and automation.
- Experience with PySpark and large-scale data processing.
- Strong experience with Azure Data Factory (ADF).
- Experience with Azure Synapse Analytics / Synapse Pipelines / Notebooks.
- Strong understanding of source-to-target data validation and reconciliation.
- Experience in data completeness, record count, schema, layout, and column validation.
- Experience in defect investigation and root cause analysis.
- Experience validating large datasets and multiple file formats.
- Experience with SQL Server / SSMS.
- Experience with Power BI dashboard/report validation.
- Strong understanding of data pipelines and ETL/ELT processes.
Cloud & Data Platform Experience
- Azure Data Factory
- Azure Synapse Analytics
- Azure Synapse Pipelines
- Azure Synapse Notebooks
- Azure Storage Accounts
- Azure Storage Containers
- Azure Cosmos DB
- Azure Privileged Identity Management (PIM)
- AWS S3
- Azure-to-AWS file transfer validation
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Preferred Skills
- Experience developing automated data validation frameworks.
- Experience building automated reporting and reconciliation utilities.
- Knowledge of ETL/ELT testing methodologies.
- Experience working with very large datasets.
- Experience in production data validation and support.
- Knowledge of cloud-based data platforms.
- Experience with Power BI data reconciliation.
- Experience working in Agile environments.
- Experience with Rally or similar Agile project management tools.
- Familiarity with Microsoft Copilot and AI-assisted productivity/automation tools.
---
Key Responsibilities at a Glance
The successful candidate will be responsible for:
- Requirement analysis and clarification
- Business rule validation
- Test planning and execution
- Data quality and data validation
- Source-to-target reconciliation
- Record count and completeness validation
- Schema and layout validation
- Column sequence validation
- Exception and reject data analysis
- Production vs. staging comparison
- SQL-based data analysis
- Python automation
- PySpark development
- Azure Data Factory pipeline testing
- Azure Synapse validation
- Azure Storage validation
- Cosmos DB validation
- AWS S3 validation
- Azure-to-AWS file transfer validation
- Power BI dashboard validation
- SQL Server metrics validation
- Automated reporting
- Defect investigation and root cause analysis
- Production job monitoring and support
- Agile delivery and stakeholder collaboration
---
Candidate Profile
We are looking for a detail-oriented, analytical, and technically strong QA/Data Validation professional who can work independently on complex data validation assignments.
The candidate should be comfortable working with large datasets, writing SQL queries, developing Python automation, analyzing PySpark datasets, validating cloud-based data pipelines, and troubleshooting data discrepancies across multiple systems.
Strong communication and stakeholder management skills are essential, as the role requires regular collaboration with technical and business teams.
---
Education
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field is preferred.
Experience
5–6 years of relevant professional experience in QA, Data Testing, Data Validation, ETL Testing, Data Quality, Data Engineering QA, or a similar role.
Location
Pan India
Employment Type
Full-Time
Keywords
QA Engineer, Data QA, Data Validation, Data Testing, ETL Testing, Data Quality, SQL, Python, PySpark, Azure Data Factory, ADF, Azure Synapse, Synapse Analytics, Synapse Pipelines, Azure Storage, Cosmos DB, AWS S3, Power BI, SQL Server, SSMS, Data Reconciliation, Source-to-Target Validation, Data Pipeline Testing, ETL QA, Automation Testing, Data Analytics, Root Cause Analysis, Agile, Rally, Cloud Data Testing, Data Engineering QA.
Shubham Vishwakarma
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