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Job 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.

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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.

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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.

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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.

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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

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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.

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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.

Description

Power BI JD

Mandatory:

• 5+ years of Power BI Report development experience.

• Building Analysis Services reporting models.

• Developing visual reports, KPI scorecards, and dashboards using Power BI desktop.

• Connecting data sources, importing data, and transforming data for Business intelligence.

• Analytical thinking for translating data into informative reports and visuals.

• Capable of implementing row-level security on data along with an understanding of application security layer models in Power BI.

• Should have an edge over making DAX queries in Power BI desktop.

• Expert in using advanced-level calculations on the data set.

• Responsible for design methodology and project documentaries.

• Should be able to develop tabular and multidimensional models that are compatible with data warehouse standards.

• Very good communication skills must be able to discuss the requirements effectively with the client teams, and with internal teams.

• Experience working with Microsoft Business Intelligence Stack having Power BI, SSAS, SSRS, and SSIS

• Mandate to have experience with BI tools and systems such as Power BI, Tableau, and SAP.

• Must have 3-4years of experience in data-specific roles.

• Have knowledge of database fundamentals such as multidimensional database design, relational database design, and more

• Knowledge of all the Power BI products (Power Bi premium, Power BI server, Power BI services, Powerquery etc)

• Grip over data analytics

• Interact with customers to understand their business problems and provide best-in-class analytics solutions

• Proficient in SQL and Query performance tuning skills

• Understand data governance, quality and security and integrate analytics with these corporate platforms

• Attention to detail and ability to deliver accurate client outputs

• Experience of working with large and multiple datasets / data warehouses

• Ability to derive insights from data and analysis and create presentations for client teams

• Experience with performance optimization of the dashboards

• Interact with UX/UI designers to create best in class visualization for business harnessing all product capabilities.

• Resilience under pressure and against deadlines.

• Proactive attitude and an open outlook.

• Strong analytical problem-solving skills

• Skill in identifying data issues and anomalies during the analysis

• Strong business acumen demonstrated an aptitude for analytics that incite action

• Ability to execute on design requirements defined by business

• Ability to understand required Power BI functionality from wireframes/ requirement documents

• Ability to architect and design reporting solutions based on client needs.

• Being able to communicate with internal/external customers, desire to develop communication and client-facing skills.

• Ability to seamlessly work with MS Excel working knowledge of pivot table and related functions

Good to have:

• Experience in working with Azure and connecting synapse with Tableau

• Demonstrate strength in data modelling, ETL development, and data warehousing

• Knowledge of leading large-scale data warehousing and analytics projects using Azure, Synapse, MS SQL DB

• Good knowledge of building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets

• Good to have knowledge of Supply Chain Domain.

Role Summary

We’re seeking a seasoned Azure Data Engineer / Consultant to lead design, build, and operationalize cloud-native data solutions. You’ll partner with external clients—from discovery through Go-Live—to ingest, model, transform, and serve high-volume data using Synapse, Fabric, PySpark, and SQL. You’ll also establish best practices around CI/CD, security, and data quality within a medallion architecture.

Required Skills (at least 4+ years of relevant experience - this is a senior-level role)

• Ability to talk to the customer
• Manage the delivery of an End-To-End Enterprise project
• Fabric (or Synapse/ADF/Databricks) Data Engineering background
• Data Warehousing experience
• Basic understanding of Agentic AI

Preferred Skills & Certifications:

• DP-600, DP-700 or equivalent certification.
• Experience with Azure Purview, Data Catalogs, or metadata management tools.
• Familiarity with orchestration frameworks (ADF, Synapse Pipelines), Spark optimization (broadcast joins, partition pruning), and data-quality frameworks (Great Expectations, custom).
• Prior consulting or client-facing engagements in enterprise environments.
• Knowledge of BI tools (Power BI).

What We Offer:

• Opportunity to own and shape cutting-edge data solutions for diverse industries.
• Collaborative, outcome-driven culture with career growth and skill-building opportunities.
• Competitive compensation and benefits package.

Job Description:

We are looking for a highly creative and talented Content Writer with expertise in crafting compelling advertisements and SEO-driven blog content. This role requires someone who is not only a wordsmith but also a creative thinker, capable of translating complex ideas into clear, persuasive, and engaging copy that captures the attention of the audience. The ideal candidate will be proficient in producing ads that resonate with customers and blogs that rank well on search engines.

Key Responsibilities:

• Ad Copywriting: Create persuasive and attention-grabbing content for various advertising platforms (Google Ads, Facebook Ads, Instagram, etc.) that drive conversions and engagement. (Must have prior experience in writing effective ad copies).
• SEO Blog Writing: Write high-quality, SEO-optimized blog posts that are informative, engaging, and rank well on search engines.
• Creative Concept Development: Work closely with the marketing team to brainstorm and develop creative content ideas and campaigns that align with brand messaging and goals.
• Content Strategy: Assist in the development of content strategies to ensure all written material is aligned with the overall marketing objectives.
• Content Editing & Proofreading: Review and edit content for clarity, consistency, and accuracy, ensuring it adheres to the brand voice and guidelines.
• Trend Monitoring: Stay up-to-date with industry trends, marketing strategies, and the latest SEO best practices to continually improve content quality and engagement.

connect me at - https://www.linkedin.com/in/babitagupta7?lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_contact_details%3B0hqjPd9SQoiOyl5Rh3PcRQ%3D%3D

We are looking for enthusiastic, customer-oriented individuals to join our team as Customer Relations Executives (CREs). This role is perfect for freshers who are eager to learn and build a career in the customer service industry. You will be responsible for handling customer interactions, providing assistance, and ensuring a seamless experience.

We are seeking an experienced Full Stack Developer with a strong background in React.js, JavaScript, TypeScript, and Node.js.

As a member of our development team, you will be responsible for designing, developing, and maintaining our web applications, ensuring high-performance and responsiveness across the entire software stack.

Responsibilities:

• Collaborate with cross-functional teams to understand requirements and develop innovative solutions using React.js, JavaScript, TypeScript, and Node.js.
• Design, develop, and deploy scalable and reliable web applications with a focus on usability, performance, and security.
• Write clean, maintainable, and efficient code using industry best practices and coding standards.
• Develop reusable components and libraries for future use.
• Collaborate with UI/UX designers to translate wireframes and mockups into responsive web interfaces.
• Optimize applications for maximum speed and scalability.
• Implement automated testing and continuous integration to ensure code quality and reliability.

Requirements:

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 5-8 years of professional experience in web development with a focus on React.js, JavaScript, TypeScript, and Node.js.
• Strong proficiency in React.js, including hooks, context API, and component lifecycle.
• Proficient in JavaScript and TypeScript with a good understanding of functional programming concepts.
• Experience with Node.js and building RESTful APIs using Express.js or similar frameworks.
• Solid understanding of web fundamentals such as HTML5, CSS3, and responsive design.
• Experience with version control systems (Git) and collaborative development workflows (GitFlow).
• Knowledge of testing frameworks (Jest, Enzyme, Mocha) and writing unit and integration tests.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills with the ability to work effectively in a team environment.