📍 Job Overview

Job Title: Senior Software Test Engineer - ETL (with a strong focus on database testing & automation)
Company: NielsenIQ
Location: Chennai, Tamil Nadu, India & Pune, Maharashtra, India
Job Type: Hybrid (On-site & Remote)
Category: Quality Assurance & Testing
Date Posted: June 20, 2025
Experience Level: Mid-Senior level (5-10 years)

🚀 Role Summary

Key Responsibilities: Design, develop, and execute comprehensive test plans for database-centric applications. Validate data accuracy, consistency, and integrity across SQL and NoSQL databases. Collaborate with cross-functional teams to ensure data quality and drive continuous improvement.
Key Skills: ETL testing, database testing, SQL, NoSQL, data warehousing, automation, Java, TypeScript, Selenium, Playwright, API testing, CI/CD, cloud platforms, analytical skills, troubleshooting, and Agile methodologies.

💻 Primary Responsibilities

🔎 Data Validation & Testing

Data Mapping & Transformation: Verify data mapping, transformation logic, and business rule implementations.
SQL Query Development: Write and maintain complex SQL queries to validate data accuracy and consistency.
Database Testing: Validate data across SQL and NoSQL databases, ensuring consistency, accuracy, and integrity.
Cloud Data Platforms: Work with cloud-based data platforms like Google BigQuery to test data pipelines and analytical queries.
Root Cause Analysis: Participate in root cause analysis for data-related issues and discrepancies.

🤝 Collaboration & Communication

Cross-Functional Collaboration: Collaborate with business, development, and QA teams to define and refine acceptance criteria.
Defect Management: Log, track, and retest defects; ensure issues are resolved in a timely manner.
Quality Metrics: Capture quality metrics to drive continuous improvement of testing practices.
Production Support: Support production releases and perform post-implementation validation.

🔄 Automation & CI/CD

Test Automation: Automate tests to increase efficiency and coverage.
Non-Functional Testing: Develop and run non-functional test automation, such as performance testing.
CI/CD Integration: Collaborate with DevOps teams to integrate testing into CI/CD pipelines.

🎓 Skills & Qualifications

📚 Education & Experience

Education: Bachelor's degree in Computer Science, Engineering, or a related field.
Experience: 4+ years of hands-on experience in ETL testing or database testing.

🛠 Required Skills

Database Proficiency: Strong SQL skills with proficiency across platforms like Oracle, SQL Server, and PostgreSQL.
NoSQL Experience: Experience testing across both relational (SQL) and non-relational (NoSQL) databases such as MongoDB.
Cloud Data Warehouses: Hands-on experience validating datasets within cloud data warehouses, particularly Google BigQuery.
ETL & Data Warehousing: Solid understanding of ETL concepts and data warehousing.
Programming Skills: Experience in programming with Java or TypeScript.
Automation Tools: Hands-on experience with automation tools like Selenium, Playwright, or equivalent.
API Testing: Skilled in automated API testing.
CI/CD & Cloud Platforms: Familiarity with CI/CD pipelines (e.g., GitLab CI/CD) and cloud platforms (preferably GCP).

🏆 Preferred Skills

Microservices Architecture: Basic understanding of microservices architecture.
Agile Methodologies: Ability to work as an embedded tester in a Scrum/Agile team.
Performance Tuning: Familiarity with performance tuning and optimization of complex SQL queries.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:
- Detailed case studies demonstrating database testing, ETL processes, and data validation projects.
- Examples of complex SQL queries and data transformation logic.
- Documentation showcasing test plans, test cases, and test data management strategies.
Technical Documentation:
- Code quality, commenting, and documentation standards for SQL scripts and test automation code.
- Version control, deployment processes, and server configuration management.
- Testing methodologies, performance metrics, and optimization techniques.

💵 Compensation & Benefits

Salary Range: INR 15-20 Lacs per annum (Estimated based on industry standards for mid-senior level QA roles in Chennai & Pune, India)

Benefits:

Dynamic and energizing work environment fostering collaboration and teamwork
Utilize cutting-edge digital technologies to stay at the forefront of innovation
Continuous training programs designed to enhance and support your professional development
Unlock avenues for both personal and career growth within the organization
Flexible working environment with adjustable working hours (hybrid model)
Volunteer time off
LinkedIn Learning
Employee-Assistance-Program (EAP)

Working Hours: 40 hours per week, with flexible hours and remote work options available.

🎯 Team & Company Context

🏢 Company Culture

Industry: Market research and consumer intelligence

Company Size: Large (10,001+ employees)

Founded: 1923 (as Nielsen; merged with IRI in 2019 to form NielsenIQ)

Team Structure:

Cross-functional teams consisting of business, development, QA, and DevOps personnel.
Agile/Scrum methodologies with sprint planning, code review, and quality assurance practices.
Deployment strategies, CI/CD pipelines, and server management processes.

Company Website: NielsenIQ

📝 Enhancement Note: NielsenIQ is a global leader in consumer intelligence, offering a dynamic and collaborative work environment. Their focus on cutting-edge technology and continuous learning makes it an attractive choice for mid-senior level QA professionals seeking to advance their careers in data validation and testing.

📈 Career & Growth Analysis

QA Career Level: Mid-Senior level (5-10 years of experience) with opportunities for technical leadership and architecture decision-making.

Reporting Structure: Embedded tester within cross-functional Agile/Scrum teams, collaborating with business, development, and DevOps personnel.

Technical Impact: Significant influence on data quality, accuracy, and consistency across database-centric applications and ETL processes.

Growth Opportunities:

Technical leadership roles, driving data quality strategies and best practices.
Architecture decision-making, contributing to the design and implementation of scalable data pipelines and warehouses.
Emerging technology adoption, staying current with the latest trends in data validation, testing, and automation.

🌐 Work Environment

Office Type: Hybrid (On-site and remote work arrangements)

Office Location(s):

Chennai, Tamil Nadu, India
Pune, Maharashtra, India

Workspace Context:

Collaborative workspaces with dedicated testing environments and tools.
Access to multiple monitors, testing devices, and performance optimization tools.
Cross-functional team interaction and knowledge-sharing opportunities.

Work Schedule: Flexible working hours with remote work options, accommodating project deadlines and maintenance windows.

📝 Enhancement Note: NielsenIQ's hybrid work environment offers a balance between on-site collaboration and remote flexibility, enabling QA professionals to maintain a healthy work-life balance while delivering high-quality data validation and testing services.

📄 Application & Technical Interview Process

Interview Process:

Technical Assessment: Coding challenges focusing on SQL queries, data validation, and test automation.
System Design Discussion: Evaluate the candidate's ability to design and implement scalable data pipelines and warehouses.
Behavioral & Cultural Fit: Assess the candidate's communication skills, problem-solving abilities, and cultural fit within the organization.
Final Evaluation: Evaluate the candidate's overall fit for the role, considering technical skills, cultural alignment, and career growth potential.

Portfolio Review Tips:

Highlight database testing, ETL processes, and data validation projects with clear documentation and case studies.
Demonstrate proficiency in SQL queries, data transformation logic, and test automation tools.
Showcase user experience design principles and accessibility standards for web applications.

Technical Challenge Preparation:

Brush up on SQL queries, data transformation logic, and test automation tools.
Familiarize yourself with NielsenIQ's data platforms, such as Google BigQuery.
Prepare for system design discussions, focusing on scalable data pipelines and warehouses.

ATS Keywords:

Programming Languages: SQL, Java, TypeScript
Web Frameworks & Libraries: Selenium, Playwright
Server Technologies: Google BigQuery, MongoDB, Oracle, SQL Server, PostgreSQL
Databases: SQL, NoSQL
Tools: CI/CD (GitLab CI/CD), cloud platforms (GCP)
Methodologies: Agile, Scrum, Kanban
Soft Skills: Analytical, troubleshooting, communication, collaboration
Industry Terms: ETL, data warehousing, data pipelines, dimensional models, data validation, data quality

🛠 Technology Stack & Web Infrastructure

Database Technologies:

SQL: Oracle, SQL Server, PostgreSQL
NoSQL: MongoDB
Cloud Data Platforms: Google BigQuery

Programming Languages:

Java
TypeScript

Automation Tools:

Selenium
Playwright

CI/CD & Cloud Platforms:

GitLab CI/CD
GCP

📝 Enhancement Note: NielsenIQ's technology stack focuses on cloud-based data platforms, relational and non-relational databases, and automation tools. This combination enables QA professionals to design, develop, and execute comprehensive test plans for database-centric applications, ensuring data accuracy, consistency, and integrity.

👥 Team Culture & Values

QA Values:

Data Quality: Prioritize data accuracy, consistency, and integrity across all applications and ETL processes.
Collaboration: Foster a collaborative work environment, encouraging knowledge-sharing and cross-functional teamwork.
Continuous Learning: Stay current with emerging trends in data validation, testing, and automation.
Innovation: Embrace new technologies and methodologies to drive continuous improvement in data quality and testing practices.

Collaboration Style:

Cross-Functional Integration: Work closely with business, development, and DevOps teams to define and refine acceptance criteria.
Code Review Culture: Participate in code reviews to ensure data quality and best practices.
Knowledge Sharing: Contribute to a culture of continuous learning and skill development.

📝 Enhancement Note: NielsenIQ's QA team values data quality, collaboration, and continuous learning. By embracing these principles, QA professionals can drive innovation and improvement in data validation and testing practices, ultimately enhancing the organization's consumer intelligence capabilities.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Data Complexity: Design and implement test plans for complex, high-volume datasets with varying data structures.
Scalability: Ensure data pipelines and warehouses can handle increased data volume and user demand.
Performance Optimization: Identify and address performance bottlenecks in data transformation, querying, and testing processes.
Data Integrity: Validate data consistency and accuracy across multiple data sources, platforms, and applications.

Learning & Development Opportunities:

Technical Skill Development: Enhance proficiency in SQL, NoSQL, cloud data platforms, and automation tools.
Emerging Technologies: Stay current with the latest trends in data validation, testing, and automation.
Leadership & Mentoring: Develop leadership and mentoring skills to drive data quality strategies and best practices.

📝 Enhancement Note: NielsenIQ's technical challenges and learning opportunities enable QA professionals to grow their careers in data validation and testing. By embracing these challenges and pursuing continuous learning, QA professionals can drive innovation and improvement in data quality and testing practices.

💡 Interview Preparation

Technical Questions:

SQL Queries: Design and optimize complex SQL queries to validate data accuracy and consistency.
Data Transformation: Demonstrate proficiency in data mapping, transformation logic, and business rule implementations.
System Design: Discuss scalable data pipelines and warehouses, considering data volume, user demand, and performance optimization.

Company & Culture Questions:

Data Quality: Explain your approach to ensuring data accuracy, consistency, and integrity across applications and ETL processes.
Collaboration: Describe your experience working with cross-functional teams, defining and refining acceptance criteria.
Continuous Learning: Share examples of how you've stayed current with emerging trends in data validation, testing, and automation.

Portfolio Presentation Strategy:

Data Validation Projects: Highlight database testing, ETL processes, and data validation projects with clear documentation and case studies.
SQL Queries: Demonstrate proficiency in SQL queries, data transformation logic, and test automation tools.
System Design: Present your approach to designing and implementing scalable data pipelines and warehouses.

📝 Enhancement Note: NielsenIQ's interview process focuses on technical proficiency, collaboration, and cultural fit. By preparing for technical questions, company and culture discussions, and portfolio presentation, QA professionals can demonstrate their qualifications and secure their next opportunity in data validation and testing.

📌 Application Steps

To apply for this Senior Software Test Engineer - ETL (with a strong focus on database testing & automation) position at NielsenIQ:

Resume Optimization: Tailor your resume to highlight relevant QA experience, skills, and achievements in ETL testing, database testing, and data validation.
Portfolio Customization: Curate a portfolio showcasing your best work in database testing, ETL processes, and data validation projects, with clear documentation and case studies.
Technical Interview Preparation: Brush up on SQL queries, data transformation logic, and test automation tools. Prepare for system design discussions, focusing on scalable data pipelines and warehouses.
Company Research: Familiarize yourself with NielsenIQ's consumer intelligence offerings, data platforms, and company culture to demonstrate your understanding and enthusiasm for the role.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and QA industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Senior Software Test Engineer - ETL ( with a strong focus on database testing & automation )