A
Quality Assurance
Accepting applicationsAsper.ai · Bengaluru, Karnataka, India
Full-Time Mid_senior Pythoncadence
Posted
21h ago
Category
Verification
Experience
Mid_senior
Country
India
Seeking individuals with demonstrated experience in Automation of Quality Assurance of Software to work alongside our product development teams and customers. This is an individual contributor role towards the release of bug free, stable application modules in Azure Cloud. Our platform is built on a Databricks lakehouse, so this role carries end-to-end accountability for quality across both the application layer and the underlying data pipelines that power it.
What will you do:
Focus is automation of software quality assurance function. Debugs software products through the use of systematic tests to develop, apply, and maintain quality standards for company products and automation of tests using any test automation tool like Cypress, Playwright, postman etc.
This position will analyse the requirements, develop and maintain the test cases and automation scripts, execute the tests cases, reports the execution details and files the defects identified in the process.
The QA Automation engineer will contribute to the automation and manual of the product to ensure we regress the application on a regular cadence.
Develop, execute and maintain test cases and automation test scripts.
Build and maintain automated data validation suites for Databricks pipelines – schema conformance, row and column-level reconciliation, null and duplicate checks, and business-rule assertions across the Bronze, Silver and Gold layers.
Write PySpark and Spark SQL queries to independently verify transformation logic against source systems and validate data lineage.
Integrate data quality checks into Databricks Workflows and CI/CD pipelines so that data regressions are caught before promotion to higher environments.
Estimation of effort needed for automation and manual test activities.
Test data creation, including generation of representative datasets for lakehouse pipeline testing.
Documents test cases for the users story requirements for traceability purposes.
Prepare reports on test automation progress, data quality coverage, and regression testing.
Ensure that the test case coverage is above 90%.
Execute testing per schedule to meet necessary testing requirement.
Experience Required:
A minimum of 3 years of experience in testing Analytics/BI applications, API testing, in-depth QA Engineering.
Good understanding of SQL and database concepts.
Minimum 1–2 years of hands-on experience with Databricks – notebooks, Delta Lake, SQL Warehouses, and job/workflow execution. This is a mandatory requirement for this role.
Working knowledge of PySpark and Spark SQL for writing data validation, reconciliation and transformation-verification logic.
Familiarity with lakehouse / medallion architecture (Bronze / Silver / Gold) and Unity Catalog concepts such as catalogs, schemas, lineage and access control.
Experience Required:
Minimum 2 years in using UI and API automation tools like Playwright/Cypress/Selenium and Postman/Python Requests Library /Rest assured etc.
Good understanding of JavaScript or Python.
Good understanding of agile software development processes including analysis, design, coding, system and user testing, problem resolution and planning.
Exposure to data quality frameworks such as Great Expectations, dbt tests or Databricks-native quality tooling will be an added advantage.
Familiarity with the Azure data stack – ADLS Gen2 and Azure Data Factory – is a plus.
CPG / Retail domain experience will be a big plus.
Show more Show less
What will you do:
Focus is automation of software quality assurance function. Debugs software products through the use of systematic tests to develop, apply, and maintain quality standards for company products and automation of tests using any test automation tool like Cypress, Playwright, postman etc.
This position will analyse the requirements, develop and maintain the test cases and automation scripts, execute the tests cases, reports the execution details and files the defects identified in the process.
The QA Automation engineer will contribute to the automation and manual of the product to ensure we regress the application on a regular cadence.
Develop, execute and maintain test cases and automation test scripts.
Build and maintain automated data validation suites for Databricks pipelines – schema conformance, row and column-level reconciliation, null and duplicate checks, and business-rule assertions across the Bronze, Silver and Gold layers.
Write PySpark and Spark SQL queries to independently verify transformation logic against source systems and validate data lineage.
Integrate data quality checks into Databricks Workflows and CI/CD pipelines so that data regressions are caught before promotion to higher environments.
Estimation of effort needed for automation and manual test activities.
Test data creation, including generation of representative datasets for lakehouse pipeline testing.
Documents test cases for the users story requirements for traceability purposes.
Prepare reports on test automation progress, data quality coverage, and regression testing.
Ensure that the test case coverage is above 90%.
Execute testing per schedule to meet necessary testing requirement.
Experience Required:
A minimum of 3 years of experience in testing Analytics/BI applications, API testing, in-depth QA Engineering.
Good understanding of SQL and database concepts.
Minimum 1–2 years of hands-on experience with Databricks – notebooks, Delta Lake, SQL Warehouses, and job/workflow execution. This is a mandatory requirement for this role.
Working knowledge of PySpark and Spark SQL for writing data validation, reconciliation and transformation-verification logic.
Familiarity with lakehouse / medallion architecture (Bronze / Silver / Gold) and Unity Catalog concepts such as catalogs, schemas, lineage and access control.
Experience Required:
Minimum 2 years in using UI and API automation tools like Playwright/Cypress/Selenium and Postman/Python Requests Library /Rest assured etc.
Good understanding of JavaScript or Python.
Good understanding of agile software development processes including analysis, design, coding, system and user testing, problem resolution and planning.
Exposure to data quality frameworks such as Great Expectations, dbt tests or Databricks-native quality tooling will be an added advantage.
Familiarity with the Azure data stack – ADLS Gen2 and Azure Data Factory – is a plus.
CPG / Retail domain experience will be a big plus.
Show more Show less