About the Role

We are seeking an experienced Senior QA Engineer with deep expertise in ETL testing, SQL, data engineering concepts, and cloud data platforms (Snowflake). The ideal candidate will have hands-on experience in designing QA strategies for modern data architectures, implementing ETL test automation, performing API testing, and leveraging AI-driven accelerators in the testing lifecycle.

This role requires someone who can operate independently, collaborate with cross-functional engineering teams, and drive quality engineering best practices across data workflows.

Key Responsibilities

QA Strategy & Test Execution

· Design, plan, and execute comprehensive test strategies for data pipelines, ETL workflows, and cloud-based data platforms.

· Validate data integration, data transformation logic, data quality, and data reconciliation across multiple layers.

· Collaborate with Data Engineers, Architects, and Product Managers to ensure functional and non-functional requirements are testable and well-defined.

ETL & Data Pipeline Testing

· Perform ETL validation across staging, raw, refined, and curated zones using Medallion Architecture principles.

· Validate data mappings, data models, data lineage, and transformation logic.

· Ensure data completeness, accuracy, timeliness, and consistency across systems.

SQL & Advanced SQL

· Write complex and optimized SQL queries to test data pipelines and transformations.

· Perform data profiling, data sampling, and reconciliation using SQL.

· Validate data against business rules and aggregation logic.

Snowflake & Cloud Data Platforms

· Test data workflows built on Snowflake SaaS platform.

· Validate Snowflake objects—tables, schemas, views, tasks, stages, and stored procedures.

· Understand and test pipelines built with Medallion Architecture / multi-zone architecture.

Automation & Tooling

· Build and maintain ETL test automation frameworks using Python or other scripting languages.

· Automate data validation, schema checks, reconciliation, and API validations.

· Perform API testing (manual and automated) using Python scripts or tools like Postman.

AI in Testing

· Explore and integrate AI/ML-based techniques to accelerate ETL testing (e.g., anomaly detection, metadata-driven test generation).

· Contribute to long-term strategy for AI-driven test automation and intelligent data quality monitoring.

Quality Governance

· Establish and enforce QA processes & best practices.

· Use test management and defect-tracking tools efficiently.

· Ensure timely reporting of test progress, risks, and quality metrics.

Test Management & Defect Management

· Work extensively with tools like JIRA, Azure DevOps, or similar for:

o Test case management

o Test cycle execution

o Defect logging, tracking, and triaging

o Sprint-level QA planning and reporting

Required Skills & Qualifications

· 7+ years of experience in QA engineering with strong focus on ETL/data testing.

· Proficiency in SQL & advanced SQL for data validation in large datasets.

· Strong understanding of data engineering principles and modern data architectures.

· Hands-on experience with Snowflake or similar cloud data warehouses.

· Experience with medallion/batch-layered architectures (Bronze–Silver–Gold).

· Experience with ETL test automation using Python or frameworks like PyTest.

· API testing experience (manual + Python-based automation).

· Strong knowledge of test management, defect management, and QA best practices.

· Exposure to or interest in AI-based test accelerators.

· Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

· Experience with orchestration tools (Airflow, DBT, Azure Data Factory, etc.).

· Knowledge of CI/CD pipelines and DevOps practices.

· Experience with version control (Git) and collaborative workflows.

About the Role

We are seeking an experienced Senior QA Engineer with deep expertise in ETL testing, SQL, data engineering concepts, and cloud data platforms (Snowflake). The ideal candidate will have hands-on experience in designing QA strategies for modern data architectures, implementing ETL test automation, performing API testing, and leveraging AI-driven accelerators in the testing lifecycle.

This role requires someone who can operate independently, collaborate with cross-functional engineering teams, and drive quality engineering best practices across data workflows.

Key Responsibilities

QA Strategy & Test Execution

· Design, plan, and execute comprehensive test strategies for data pipelines, ETL workflows, and cloud-based data platforms.

· Validate data integration, data transformation logic, data quality, and data reconciliation across multiple layers.

· Collaborate with Data Engineers, Architects, and Product Managers to ensure functional and non-functional requirements are testable and well-defined.

ETL & Data Pipeline Testing

· Perform ETL validation across staging, raw, refined, and curated zones using Medallion Architecture principles.

· Validate data mappings, data models, data lineage, and transformation logic.

· Ensure data completeness, accuracy, timeliness, and consistency across systems.

SQL & Advanced SQL

· Write complex and optimized SQL queries to test data pipelines and transformations.

· Perform data profiling, data sampling, and reconciliation using SQL.

· Validate data against business rules and aggregation logic.

Snowflake & Cloud Data Platforms

· Test data workflows built on Snowflake SaaS platform.

· Validate Snowflake objects—tables, schemas, views, tasks, stages, and stored procedures.

· Understand and test pipelines built with Medallion Architecture / multi-zone architecture.

Automation & Tooling

· Build and maintain ETL test automation frameworks using Python or other scripting languages.

· Automate data validation, schema checks, reconciliation, and API validations.

· Perform API testing (manual and automated) using Python scripts or tools like Postman.

AI in Testing

· Explore and integrate AI/ML-based techniques to accelerate ETL testing (e.g., anomaly detection, metadata-driven test generation).

· Contribute to long-term strategy for AI-driven test automation and intelligent data quality monitoring.

Quality Governance

· Establish and enforce QA processes & best practices.

· Use test management and defect-tracking tools efficiently.

· Ensure timely reporting of test progress, risks, and quality metrics.

Test Management & Defect Management

· Work extensively with tools like JIRA, Azure DevOps, or similar for:

o Test case management

o Test cycle execution

o Defect logging, tracking, and triaging

o Sprint-level QA planning and reporting

Required Skills & Qualifications

· 7+ years of experience in QA engineering with strong focus on ETL/data testing.

· Proficiency in SQL & advanced SQL for data validation in large datasets.

· Strong understanding of data engineering principles and modern data architectures.

· Hands-on experience with Snowflake or similar cloud data warehouses.

· Experience with medallion/batch-layered architectures (Bronze–Silver–Gold).

· Experience with ETL test automation using Python or frameworks like PyTest.

· API testing experience (manual + Python-based automation).

· Strong knowledge of test management, defect management, and QA best practices.

· Exposure to or interest in AI-based test accelerators.

· Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

· Experience with orchestration tools (Airflow, DBT, Azure Data Factory, etc.).

· Knowledge of CI/CD pipelines and DevOps practices.

· Experience with version control (Git) and collaborative workflows.