ETL QA Engineer

P99soft Pvt Ltd; Hyderabad; Hybrid; INR 1,400,000 - 2,100,000; Full time

Job summary

P99Soft Pvt Ltd in Hyderabad is seeking an experienced ETL QA Engineer with 5+ years of experience in ETL/Data Warehouse testing, SQL, and automation to ensure data pipelines' quality.

You will validate data across sources, develop automated tests in Python/Selenium, and explore AI-assisted testing to improve coverage and efficiency.

Qualifications

• 5+ years of experience in ETL/Data Warehouse Testing or Data QA.
• Strong hands-on experience with ETL testing concepts and methodologies.
• Strong proficiency in SQL and database testing.
• Experience with data validation, reconciliation, transformation testing, and data quality testing.

Responsibilities

• Design and execute test strategies for ETL pipelines, data warehouses, and data integration workflows.
• Validate data extraction, transformation, and loading processes across multiple data sources and target systems.
• Perform source-to-target validation, data reconciliation, transformation testing, and data quality checks.
• Write complex SQL queries to validate data accuracy, completeness, consistency, and integrity.
• Develop and maintain automated test scripts/frameworks for ETL and data validation.

Skills

ETL/Data Warehouse testing

SQL

Test automation

Python

GenAI / AI-assisted testing

Tools

Python

Selenium

PyTest

SQL Server

PostgreSQL

MySQL

Oracle

Snowflake

Databricks

Job description

ETL QA Engineer – 5+ Years

Location: Hyderabad

Work Mode: Hybrid – 3 Days Work From Office per Week

Experience: 5+ Years

Notice Period: Immediate Joiners Preferred

About P99Soft

P99Soft is a technology and digital engineering company delivering innovative software solutions across cloud, data, AI, and modern application engineering. We are looking for an experienced ETL QA Engineer who can ensure the quality, reliability, and accuracy of data pipelines and analytics solutions while leveraging automation and emerging AI/GenAI tools to improve testing efficiency.

About The Role

We are looking for a ETL QA Engineer with 5+ years of experience in ETL/Data Warehouse testing, SQL, and test automation. The ideal candidate should have strong expertise in validating data pipelines, transformations, data quality, and end-to-end ETL workflows.

The candidate should also have an understanding of GenAI, AI-assisted testing, and modern AI tools, and be comfortable exploring how AI can be used to improve test case generation, data validation, automation, defect analysis, and overall QA productivity.

Key Responsibilities

• Design and execute test strategies for ETL pipelines, data warehouses, and data integration workflows.
• Validate data extraction, transformation, and loading processes across multiple data sources and target systems.
• Perform source-to-target validation, data reconciliation, transformation testing, and data quality checks.
• Write complex SQL queries to validate data accuracy, completeness, consistency, and integrity.
• Develop and maintain automated test scripts/frameworks for ETL and data validation.
• Identify opportunities to automate repetitive data validation and regression testing activities.
• Perform functional, integration, regression, and end-to-end testing of data pipelines.
• Validate data transformations, business rules, mappings, joins, aggregations, and calculated fields.
• Analyze defects, perform root-cause analysis, and work closely with developers, data engineers, and business teams to resolve issues.
• Validate data across databases, APIs, files, and cloud-based data platforms.
• Participate in test planning, estimation, test case design, execution, and reporting.
• Contribute to improving QA processes, automation coverage, and overall testing efficiency.
• Explore and utilize GenAI/AI tools for test case generation, test data creation, SQL assistance, defect analysis, documentation, and automation.
• Stay updated with emerging AI-assisted QA and testing practices and identify opportunities to integrate them into the QA lifecycle.

Required Skills & Experience

• 5+ years of experience in ETL/Data Warehouse Testing or Data QA.
• Strong hands‑on experience with ETL testing concepts and methodologies.
• Strong proficiency in SQL and database testing.
• Experience with data validation, reconciliation, transformation testing, and data quality testing.
• Good understanding of Data Warehousing concepts, including fact/dimension tables, ETL workflows, joins, aggregations, and data models.
• Experience with test automation using tools/frameworks such as Python, Selenium, PyTest, or similar automation technologies.
• Experience in automating data validation and regression test scenarios.
• Experience working with databases such as SQL Server, PostgreSQL, MySQL, Oracle, or similar.
• Exposure to cloud/data platforms such as AWS, Azure, Snowflake, Databricks, or similar is preferred.
• Good understanding of QA processes, defect management, and Agile methodologies.
• Hands‑on experience with GenAI/AI tools such as ChatGPT, Claude, Gemini, GitHub Copilot, or similar AI‑assisted development/testing tools.
• Ability to use AI tools effectively for test design, SQL generation/optimization, automation assistance, test data generation, and defect analysis.

Good to Have

• Experience testing cloud-based ETL/data pipelines.
• Exposure to Snowflake, Databricks, AWS, Azure, or GCP.
• Experience with ETL tools such as Informatica, Talend, SSIS, dbt, or similar.
• Knowledge of Python-based data testing/automation.
• Exposure to CI/CD pipelines and DevOps practices.
• Exposure to LLMs, RAG, AI agents, or AI-based testing solutions.
• Experience integrating AI tools into QA workflows or building AI-assisted testing utilities.

What We Are Looking For

• Strong analytical and problem-solving skills.
• Ability to independently understand complex data flows and business requirements.
• Strong attention to detail and data accuracy.
• Ability to identify gaps in test coverage and proactively improve testing approaches.
• Good communication and collaboration skills.
• Willingness to experiment with AI/GenAI tools and adopt modern QA practices.
• Ability to work effectively in a fast‑paced, collaborative environment.