Nagpur, Maharashtra
Job Summary
ETL/ELT experience with Azure Databricks or a comparable data integration platform ( Must Have ).
Proficiency in Python for development and automation activities ( Must Have ).
Strong SQL skills for data validation, analysis, and troubleshooting ( Must Have ).
Good understanding of CI/CD processes and experience in automating data pipelines.
Hands-on experience with GitHub for source control, collaboration, and deployment workflows.
Ability to design and develop automated data validation frameworks using Python and SQL, and build validation pipelines to verify data integrity following deployments.
The ideal candidate should be self-motivated, collaborative, and capable of driving automation and continuous improvement initiatives within the data engineering ecosystem.
Strong expertise in test automation, data validation, and cloud-based ETL systems
Hands-on experience in Azure cloud environments, CI/CD automation, data testing, and Embedded Power BI (PBI) reporting validation using modern automation frameworks like Playwright.
Design, develop, and maintain automation test frameworks using Playwright
Implement Page Object Model (POM) and automation best practices
Perform data validation testing for Azure ETL pipelines (ADF / Databricks / Synapse)
Validate Embedded Power BI (PBI) reports and dashboards
Perform data reconciliation between Power BI visuals and backend data using SQL
Integrate automation suites into CI/CD pipelines (Azure DevOps / GitHub Actions / Jenkins)
Execute end-to-end testing for Embedded PBI solutions
Analyze test results, log defects, and perform root cause analysis
Experience in Embedded Power BI testing, Power BI REST API, DAX basics, Performance testing, Docker / Containers
Understanding of requirements and processes:
There should not be gaps in understanding requirements and ETL testing processes, that can impact task execution.
Attention to basic validation:
Some basic ETL validation checks are being should not be missed, leading to rework.
Automation skills:
strong automation knowledge to improve efficiency and reduce dependency.
Task completion timelines:
Tasks should not take longer than expected, and frequent follow-ups should not be required for progress tracking.
Quality gaps in delivery:
A major bug not to be missed in the customer delivery epic, which can cause concern from a delivery and quality standpoint.
Be more proactive in understanding requirements—clarify doubts early instead of waiting.
Perform thorough validation checks before marking tasks as complete.
Work on improving automation skills (we can align on support areas if needed).
Provide regular/daily updates on progress and blockers without requiring follow-ups.
Overall, the expectation is to take full ownership of assigned work and be more proactive in execution. This will help improve delivery quality and reduce dependency on constant tracking.
Key Responsibilities
ETL/ELT experience with Azure Databricks or a comparable data integration platform ( Must Have ).
Proficiency in Python for development and automation activities ( Must Have ).
Strong SQL skills for data validation, analysis, and troubleshooting ( Must Have ).
Good understanding of CI/CD processes and experience in automating data pipelines.
Hands-on experience with GitHub for source control, collaboration, and deployment workflows.
Ability to design and develop automated data validation frameworks using Python and SQL, and build validation pipelines to verify data integrity following deployments.
The ideal candidate should be self-motivated, collaborative, and capable of driving automation and continuous improvement initiatives within the data engineering ecosystem.
Strong expertise in test automation, data validation, and cloud-based ETL systems
Hands-on experience in Azure cloud environments, CI/CD automation, data testing, and Embedded Power BI (PBI) reporting validation using modern automation frameworks like Playwright.
Design, develop, and maintain automation test frameworks using Playwright
Implement Page Object Model (POM) and automation best practices
Perform data validation testing for Azure ETL pipelines (ADF / Databricks / Synapse)
Validate Embedded Power BI (PBI) reports and dashboards
Perform data reconciliation between Power BI visuals and backend data using SQL
Integrate automation suites into CI/CD pipelines (Azure DevOps / GitHub Actions / Jenkins)
Execute end-to-end testing for Embedded PBI solutions
Analyze test results, log defects, and perform root cause analysis
Experience in Embedded Power BI testing, Power BI REST API, DAX basics, Performance testing, Docker / Containers
Understanding of requirements and processes:
There should not be gaps in understanding requirements and ETL testing processes, that can impact task execution.
Attention to basic validation:
Some basic ETL validation checks are being should not be missed, leading to rework.
Automation skills:
strong automation knowledge to improve efficiency and reduce dependency.
Task completion timelines:
Tasks should not take longer than expected, and frequent follow-ups should not be required for progress tracking.
Quality gaps in delivery:
A major bug not to be missed in the customer delivery epic, which can cause concern from a delivery and quality standpoint.
Be more proactive in understanding requirements—clarify doubts early instead of waiting.
Perform thorough validation checks before marking tasks as complete.
Work on improving automation skills (we can align on support areas if needed).
Provide regular/daily updates on progress and blockers without requiring follow-ups.
Overall, the expectation is to take full ownership of assigned work and be more proactive in execution. This will help improve delivery quality and reduce dependency on constant tracking.
Skill Requirements
ETL/ELT experience with Azure Databricks or a comparable data integration platform ( Must Have ).
Proficiency in Python for development and automation activities ( Must Have ).
Strong SQL skills for data validation, analysis, and troubleshooting ( Must Have ).
Good understanding of CI/CD processes and experience in automating data pipelines.
Hands-on experience with GitHub for source control, collaboration, and deployment workflows.
Ability to design and develop automated data validation frameworks using Python and SQL, and build validation pipelines to verify data integrity following deployments.
The ideal candidate should be self-motivated, collaborative, and capable of driving automation and continuous improvement initiatives within the data engineering ecosystem.
Strong expertise in test automation, data validation, and cloud-based ETL systems
Hands-on experience in Azure cloud environments, CI/CD automation, data testing, and Embedded Power BI (PBI) reporting validation using modern automation frameworks like Playwright.
Design, develop, and maintain automation test frameworks using Playwright
Implement Page Object Model (POM) and automation best practices
Perform data validation testing for Azure ETL pipelines (ADF / Databricks / Synapse)
Validate Embedded Power BI (PBI) reports and dashboards
Perform data reconciliation between Power BI visuals and backend data using SQL
Integrate automation suites into CI/CD pipelines (Azure DevOps / GitHub Actions / Jenkins)
Execute end-to-end testing for Embedded PBI solutions
Analyze test results, log defects, and perform root cause analysis
Experience in Embedded Power BI testing, Power BI REST API, DAX basics, Performance testing, Docker / Containers
Understanding of requirements and processes:
There should not be gaps in understanding requirements and ETL testing processes, that can impact task execution.
Attention to basic validation:
Some basic ETL validation checks are being should not be missed, leading to rework.
Automation skills:
strong automation knowledge to improve efficiency and reduce dependency.
Task completion timelines:
Tasks should not take longer than expected, and frequent follow-ups should not be required for progress tracking.
Quality gaps in delivery:
A major bug not to be missed in the customer delivery epic, which can cause concern from a delivery and quality standpoint.
Be more proactive in understanding requirements—clarify doubts early instead of waiting.
Perform thorough validation checks before marking tasks as complete.
Work on improving automation skills (we can align on support areas if needed).
Provide regular/daily updates on progress and blockers without requiring follow-ups.
Overall, the expectation is to take full ownership of assigned work and be more proactive in execution. This will help improve delivery quality and reduce dependency on constant tracking.
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