Data Analyst

Key Responsibilities

• Collect, consolidate and manage data from various internal systems, SaaS platforms, CRM/ERP modules, logs, and databases.
• Generate daily, weekly, monthly, and ad-hoc MIS reports and dashboards for operations, sales, finance, customer-support or other relevant teams.
• Use advanced Excel to build, maintain, and manage complex spreadsheets — including pivot tables, VLOOKUP / HLOOKUP / INDEX-MATCH, SUMIF/COUNTIF, conditional formatting, charts/graphs, macros/VBA (if needed) to automate routine reporting tasks.
• Validate and clean data — ensure data integrity, consistency; identify and rectify discrepancies or anomalies.
• Collaborate with cross-functional teams (product, operations, support, finance, sales) to understand their data/reporting needs and deliver appropriate reports/insights.
• Provide ad-hoc data analysis or custom reporting as required by management or US stakeholders.
• Maintain documentation of reporting processes, data definitions/SOPs, report templates, and standard workflows.
• Ensure timely delivery of reports — especially considering night-shift schedule — so that US-based stakeholders receive data at start of their business day.
• Identify opportunities for process improvement and automation to make MIS reporting more efficient and reliable.

Required Skills & Qualifications

• Bachelor’s degree (in IT, Computer Science, Business, Statistics, or related field) or equivalent.
• Proven experience (1–3 years or as per company requirement) in MIS, data reporting/analysis, or similar role.
• Strong proficiency in Microsoft Excel — including advanced formulas/functions (VLOOKUP/HLOOKUP/INDEX-MATCH), pivot tables, charts/graphs, conditional formatting, data cleaning, data validation.
• Ability to build and maintain dashboards/reports; comfortable using Excel for recurring and ad-hoc reports.
• Strong analytical and problem-solving skills; ability to work with large datasets, identify trends, anomalies, and draw insights.
• Good communication skills — ability to coordinate with different teams/stakeholders, understand requirements, and present data/insights clearly.
• Readiness for night-shift work from office; ability to work independently, meet deadlines and manage time effectively.

Strong Data Analyst Profile with advanced Excel and SQL expertise

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Mandatory (Experience 1): Must have 4+ years of overall experience as a hands-on Data Analyst

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Mandatory (Tech skill 1): Must be highly proficient in advanced Excel — complex functions, macros, calculations, and pivots

4

Mandatory (Tech skill 2): Must have strong hands-on SQL and a good understanding of relational database concepts

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Mandatory (Tech skill 3): Must be able to automate routine tasks using Python (for automation purposes)

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Mandatory (Skill 1): Must have exceptional analytical, problem-solving, and logical skills, with strong attention to detail and accuracy

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Mandatory (Skill 2): Must be able to understand complex data and business logic and convert it into a model (the role models complex utility tariffs, rates, and programs)

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Mandatory (Communication): Must have strong verbal and written communication, able to work independently with India- and US-based team members and articulate problems and solutions over calls and email.

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Mandatory (Location): Must be based locally in Pune (or the nearby Maharashtra belt — Mumbai, Nagpur), as the final round is in person

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Preferred (Domain): Experience in the Energy/Utility industry and familiarity with basic utility (electrical/gas) tariff concepts

Experience Required: Minimum 8 Years

About the Role:

Key Responsibilities

• Design and manage organization-wide MIS reports, dashboards, and executive reports.
• Develop automated reporting solutions using Python, VBA Macros, Power Query, and Advanced Excel.
• Analyze large datasets and provide business insights to leadership.

Eligibility Criteria

• 5+ years of experience in Business Intelligence, MIS, Business Analytics, or Data Analytics.
• Strong analytical and logical thinking with the ability to understand business requirements and convert data into meaningful business insights.
• Experience in developing dashboards and management reports using any Business Intelligence tool (Power BI, Tableau, Zoho Analytics, etc.).

Apply Now:

https://zfrmz.com/pAKb2ynfomIsuNwRfRbV?utm_source=cutshort

At Nineleaps, we work on bleeding-edge technology with class-leading engineering practices on products that touch the lives of millions of users. We endeavor on doing things the right way, while also promoting a culture of excellence.

About the Role:

We are looking for a Data Analyst with strong analytical and problem-solving skills to transform complex data into meaningful, actionable business insights. The role involves working with large datasets, conducting deep-dive analysis, driving automation, and supporting data-driven product and business decisions.

Key Responsibilities:

• Analyse historical and large datasets to understand data sources, identify trends and patterns, and uncover meaningful insights.
• Write complex SQL queries and leverage Python to perform data analysis, ad hoc investigations, and solve business problems.
• Create reports and translate analytical findings into clear, concise, and actionable recommendations for stakeholders.
• Identify opportunities to drive automation and process improvements, improving efficiency and reducing manual effort.
• Communicate data-driven insights effectively to both technical and non-technical stakeholders in a clear and impactful manner.
• Maintain accurate documentation, ensure high-quality deliverables, and consistently meet defined timelines.

Requirements:

• 3–6 years of experience in Data Analytics, Business Intelligence, Data Engineering, or a similar analytical role.
• Strong hands-on expertise in Python and advanced SQL, with the ability to work with and analyse large datasets.
• Experience working with Google Sheets, and implementing automation through data pipelines or workflows.
• Strong analytical and problem-solving skills, with the ability to interpret complex data and derive actionable insights.
• Excellent communication skills with the ability to effectively present methods, results, and recommendations to stakeholders.
• Ability to collaborate effectively with remote and geographically distributed teams across different time zones.

Company Link: https://www.nineleaps.com/

Company LinkedIn: https://www.linkedin.com/company/nineleaps/

Role Overview

We are seeking a skilled Senior Data Analyst with substantial hands-on experience in Source System Analysis, data mapping, data modeling, SQL analysis, and integration testing. The ideal candidate will support seamless data flow across business applications, ensure data accuracy, and contribute to system improvements.

Key Responsibilities

• Conduct detailed data mapping between source and target systems, ensuring consistency and accuracy.
• Build and maintain data models to support business processes, reporting needs, and integration workflows.
• Write and optimize SQL queries for data validation, analysis, migration, and troubleshooting.
• Work closely with cross-functional teams to gather data requirements and understand business logic.
• Develop and execute integration test plans, including functional, data validation, and regression testing.
• Support data migration activities, including extraction, transformation, and loading (ETL).
• Monitor and troubleshoot integration issues, ensuring timely resolution.
• Document integration flows, data dictionaries, mapping catalogs, and knowledge artifacts.
• Ensure integration security, data integrity, and compliance with organizational standards.

Required Skills & Qualifications

• 5+ years of experience insource system integrations, data mapping, and system-to-system data workflows.
• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• Hands-on experience with data mapping, data modelling, and database schema design.
• Proficiency in SQL (complex joins, stored procedures, performance tuning).
• Experience with ETL tools or integration platforms (Informatica, Boomi, MuleSoft, Talend, Pentaho).
• Strong analytical and problem-solving skills.
• Experience with API-based integrations (REST/SOAP) is a plus.
• Familiarity with testing methodologies for integrations and data validation.
• Ability to work with business users, understand workflows, and translate requirements into technical solutions.

Preferred Qualifications

• Experience with cloud ERP or cloud integration (AWS/Azure/GCP).
• Knowledge of JSON, XML, CSV transformation.
• Exposure to scripting languages (Python, Shell).
• Experience in Agile/Scrum environments.
• Prior involvement in ERP implementation or upgrade projects.

Competencies

• Tech Savvy - Anticipating and adopting innovations in business-building digital and technology applications.
• Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
• Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
• Customer Focus - Building strong customer relationships and delivering customer-centric solutions.
• Optimize Work Processes - Knowing the most effective and efficient processes to get things done, with a focus on continuous improvement.

Why Join Us?

• Be part of a collaborative and agile team driving cutting-edge AI and data engineering solutions.
• Work on impactful projects that make a difference across industries.
• Opportunities for professional growth and continuous learning.
• Competitive salary and benefits package.

Application Details

Ready to make an impact? Apply today and become part of the QX Impact team!

Hiring for Data Analyst

Exp : 5 - 7 yrs

Edu : BE/B.Tech

Work Location : Noida WFO

Skills :

Expertise in SQL Server, including database design, performance tuning, query optimization, and security.

Hands-on experience developing ETL solutions using SSIS, Azure Data Factory (ADF), and Python.

Job Description: 

Join us at Springer Capital, a corporate inclusion training company dedicated to promoting diversity and equity within the workplace.  

Our mission is to transform organizational cultures and achieve justice by creating environments where every individual is valued and feels a sense of belonging. Through providing training for workplace inclusion, understanding microaggressions, mitigating bias, and cultural literacy, Springer Capital seeks to eliminate bias and establish a just, fair working environment.  

As a Data Automation Intern, you will be focusing on researching and developing tools and workflow that automate some parts and processes of our business automation. Since business automation is important throughout the firm, you will have the opportunity to collaborate with various teams across Springer Capital. 

Key Responsibilities:  

• Collect data from various sources, including databases, APIs, and web scraping tools.    
• Clean and process raw data to ensure it is accurate and consistent.     
• Analyze data to extract insights using computational tools, such as Excel, SQL, and Python.   
• Communicate insights in a clear and concise manner with the manager alongside your progress.    
• Implement solutions based on insights you discovered to improve Springer Capital business’ processes or solve problems for client. 

  Qualifications: 

• Passion for Inclusion: A strong commitment to fighting inequality and promoting inclusion in the workplace. 
• Educational Background: Currently enrolled in or recently graduated from a degree program related to social sciences, human resources, business, statistics.  
• Communication Skills: Excellent written and verbal communication skills. Ability to create clear and engaging content. 
• Organizational Skills: Detail-oriented and highly organized. Ability to manage multiple tasks and deadlines effectively. 
• Analytical and Business Software Skills: Proficiency in business software such as Excel, PowerPoint, and Word with a strong preference of knowledge and experience in data analytics  

 Compensation and Expectations: 

• This internship is remote and unpaid. Interns are expected to work 15-20 hours a week. 

Position title: Business Intelligence and Data Analyst

Summary -

• Operate and continuously improve the CARS/BI backend within the global Enterprise Data and Business Intelligence platform, ensuring system stability, continuous data loading, and Warehouse the timely delivery of business-critical reports.

• Own the underlying data foundation — SQL pipelines, relational data models, and data warehouse structures — that internal reporting teams and global Data Management initiatives depend on, and act as 1st and 2nd level support for internal customers such as SCM and Sales.

• The role is needed to secure daily BI availability in the Asian time zone, reduce the risk of late or incorrect reporting, and add dedicated database and ETL capacity to the global BI team, including support to the Data Centre team on infrastructure-related issues.

Responsibilities:

• Operate, monitor, and maintain the CARS/BI data integration and data processing workflows, ensuring continuous data loading and stable daily business operations.

• Provide 1st and 2nd level support for CARS/BI, including user support, incident handling, and root-cause analysis of data issues.

• Develop, maintain, and optimise SQL-based data pipelines and transformations within the Enterprise Data Warehouse.

• Design and maintain relational data models and data warehouse structures for CARS BI.

• Integrate data from ERP and other enterprise systems into the BI backend.

• Ensure data consistency, integrity, and performance at the database level, including query tuning and database efficiency improvements.

• Administer the BI technical environment and contribute to its continuous enhancement as part of a global team.

• Support reporting teams by providing structured, reliable datasets and safeguarding the timely delivery of business-critical reports.

• Contribute to global Data Management initiatives, with a focus on Master Data Management and the development of a Common Data Model within the Business Integration Platform.

• Collaborate with the Data Centre team to resolve infrastructure-related issues affecting BI availability.

Education and Experience:

Bachelor's degree in Engineering (BE/B.Tech) or equivalent in Computer Science, Information Technology, or a comparable technical field.

Minimum 3 years of relevant professional experience in Business Intelligence, data warehousing, or database development, including hands-on SQL and ETL work in an enterprise environment.

Experience supporting business users in a global or multi-time-zone IT organisation is an advantage.

Competencies:

• Knowledge about either BI platforms (e.g. IBM Cognos BI suite) or ETL tools (e.g. Informatica PowerCenter)

• Database, data modeling and SQL (preferred Oracle PLSQL)

• Good analytical and communication skills

• Teamplayer

• English

• Strong hands-on experience with SQL (advanced level)

• Solid understanding of relational databases and data warehouse concepts

• Experience with ETL tools and database technologies (e.g., Oracle, SQL Server, SAP BW,Informatica or similar)

• Performance tuning and query optimization skills

• Structured, detail-oriented, and quality-focused working style

• Good understanding of enterprise data flows (SAP/CARS is a plus)

Key Interfaces and Stakeholders:

Only internal customers with different topics: e.g. SCM, Sales etc.

Geography to cover and Travel requirements:

Asian Time Zone, Sometimes travel is required

Behavioral Characteristics

Reliability and accountability — the role safeguards daily reporting availability, so dependable ownership of monitoring and issue follow-up is expected. Structured, analytical problem solving with the patience for thorough root-cause analysis. Service orientation and clear communication towards internal customers such as SCM and Sales. Initiative taking and self-reliance, given largely independent work in the Asian time zone. Cooperation and team spirit within a globally distributed BI team across cultures and time zones. Flexibility and resilience under time pressure, including occasional off-hours support during critical data loads. Integrity and discretion when handling confidential business data. Quality focus and attention to detail, with a continuous improvement mindset.

Interview process

2 rounds - Virtual interview and 1 round Face to Face

Any other Criteria

• Notice Period: Below 60 or 90 Days . No Negotiation on Notice Period
• Gender: Female and Male; Female preferred
• Qualification:Bachelor's degree in Engineering (BE/B.Tech) or equivalent in Computer Science, Information Technology, 

This role will be permanent with NAM info and deploy to client location Hyderabad & Pune.

Work Mode: WORK FROM OFFICE

Role Descriptions:

• Perform detailed data analysis and support business decision-making
• Gather and document business requirements and translate them into technical specifications
• Work closely with stakeholders to define data needs and reporting requirements
• Create user stories, functional specifications, and support UAT activities
• Ensure alignment between business objectives and data solutions

Required Skills:

• Strong expertise in SQL and data querying
• Proven experience in data analysis, requirement gathering, and stakeholder management
• Ability to translate business requirements into technical solutions and user stories
• Good understanding of data models, reporting, and analytics concepts

Skills: Business Analysis~ORACLE SQL

Locations: ~HYDERABAD~PUNE~

Desire candidate

• Candidate should have valid PF.

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.