QA & Data Validation Engineer

– 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. --- 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. --- 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. --- 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. --- 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. --- 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. --- 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 wor