Manager- Data Engineer
About this role
Key responsibilities
- Design, implement, and optimize end-to-end ETL pipelines in Microsoft Fabric, from ingestion through multi-stage transformations to data loading and delivery.
- Build pipelines and notebooks using Python and PySpark; implement data validation, error handling, and quality controls.
- Collaborate with business analysts and stakeholders to translate requirements and accounting logic into transformation rules and data solutions.
- Work with data architects to design schemas and data models (star/snowflake) and, where needed, OLAP cubes aligned to application requirements.
- Ensure efficient, accurate processing from source systems into Fabric’s data layers; optimize performance and scalability (partitioning, indexing, resource tuning).
- Leverage modern tooling and practices (e.g., Azure DevOps for boards, repos, CI/CD); uphold consistent development standards across the global team.
- Conduct unit, integration, and end-to-end testing; troubleshoot and continuously improve ETL processes.
- Maintain comprehensive, up-to-date documentation (processes, sources, data flows, models) accessible to stakeholders; ensure compliance with policies and standards.
Required skills and experience
- High proficiency with Microsoft Fabric ETL; experience with related tools such as Azure Data Factory.
- Strong SQL for extraction, transformation, and querying; hands-on with SQL Server, Azure SQL Database, and Synapse Analytics.
- Data engineering fundamentals: data modeling and schema design (star/snowflake), transformation, and optimization for warehousing/analytics; experience with OLAP where applicable.
- Proficiency in Python and PySpark for ETL development within Fabric notebooks and pipelines.
- Experience loading and optimizing data at scale in Fabric and prior exposure to Azure Synapse and Azure Data Lake.
- Familiarity with Azure DevOps workflows (work tracking, version control, pipelines) and modern development practices.
- Rigorous testing approach (unit, integration, E2E), with robust data validation and error-handling procedures.
- Strong collaboration and communication in globally distributed teams; ability to share best practices and evolve standards based on feedback and industry trends.
Key responsibilities
- Design, implement, and optimize end-to-end ETL pipelines in Microsoft Fabric, from ingestion through multi-stage transformations to data loading and delivery.
- Build pipelines and notebooks using Python and PySpark; implement data validation, error handling, and quality controls.
- Collaborate with business analysts and stakeholders to translate requirements and accounting logic into transformation rules and data solutions.
- Work with data architects to design schemas and data models (star/snowflake) and, where needed, OLAP cubes aligned to application requirements.
- Ensure efficient, accurate processing from source systems into Fabric’s data layers; optimize performance and scalability (partitioning, indexing, resource tuning).
- Leverage modern tooling and practices (e.g., Azure DevOps for boards, repos, CI/CD); uphold consistent development standards across the global team.
- Conduct unit, integration, and end-to-end testing; troubleshoot and continuously improve ETL processes.
- Maintain comprehensive, up-to-date documentation (processes, sources, data flows, models) accessible to stakeholders; ensure compliance with policies and standards.
Required skills and experience
- High proficiency with Microsoft Fabric ETL; experience with related tools such as Azure Data Factory.
- Strong SQL for extraction, transformation, and querying; hands-on with SQL Server, Azure SQL Database, and Synapse Analytics.
- Data engineering fundamentals: data modeling and schema design (star/snowflake), transformation, and optimization for warehousing/analytics; experience with OLAP where applicable.
- Proficiency in Python and PySpark for ETL development within Fabric notebooks and pipelines.
- Experience loading and optimizing data at scale in Fabric and prior exposure to Azure Synapse and Azure Data Lake.
- Familiarity with Azure DevOps workflows (work tracking, version control, pipelines) and modern development practices.
- Rigorous testing approach (unit, integration, E2E), with robust data validation and error-handling procedures.
- Strong collaboration and communication in globally distributed teams; ability to share best practices and evolve standards based on feedback and industry trends.
Key responsibilities
- Design, implement, and optimize end-to-end ETL pipelines in Microsoft Fabric, from ingestion through multi-stage transformations to data loading and delivery.
- Build pipelines and notebooks using Python and PySpark; implement data validation, error handling, and quality controls.
- Collaborate with business analysts and stakeholders to translate requirements and accounting logic into transformation rules and data solutions.
- Work with data architects to design schemas and data models (star/snowflake) and, where needed, OLAP cubes aligned to application requirements.
- Ensure efficient, accurate processing from source systems into Fabric’s data layers; optimize performance and scalability (partitioning, indexing, resource tuning).
- Leverage modern tooling and practices (e.g., Azure DevOps for boards, repos, CI/CD); uphold consistent development standards across the global team.
- Conduct unit, integration, and end-to-end testing; troubleshoot and continuously improve ETL processes.
- Maintain comprehensive, up-to-date documentation (processes, sources, data flows, models) accessible to stakeholders; ensure compliance with policies and standards.
Required skills and experience
- High proficiency with Microsoft Fabric ETL; experience with related tools such as Azure Data Factory.
- Strong SQL for extraction, transformation, and querying; hands-on with SQL Server, Azure SQL Database, and Synapse Analytics.
- Data engineering fundamentals: data modeling and schema design (star/snowflake), transformation, and optimization for warehousing/analytics; experience with OLAP where applicable.
- Proficiency in Python and PySpark for ETL development within Fabric notebooks and pipelines.
- Experience loading and optimizing data at scale in Fabric and prior exposure to Azure Synapse and Azure Data Lake.
- Familiarity with Azure DevOps workflows (work tracking, version control, pipelines) and modern development practices.
- Rigorous testing approach (unit, integration, E2E), with robust data validation and error-handling procedures.
- Strong collaboration and communication in globally distributed teams; ability to share best practices and evolve standards based on feedback and industry trends.
Frequently Asked Questions
Is the salary disclosed for the Manager- Data Engineer position at KPMG Global Services?
The salary for this Manager- Data Engineer role at KPMG Global Services is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Where is the Manager- Data Engineer position at KPMG Global Services located?
This Manager- Data Engineer role at KPMG Global Services is based in Bangalore, Karnataka, India. The position is listed as on-site or hybrid. Check the full job description or apply directly to confirm the work arrangement.
Is the Manager- Data Engineer role at KPMG Global Services full-time or part-time?
This is listed as a Full time position. It is posted as a Manager- Data Engineer role at KPMG Global Services.
How do I apply for the Manager- Data Engineer position at KPMG Global Services?
Click the "Apply Now" button on this page. You will be redirected to KPMG Global Services's official application portal hosted on oraclecloud where you can submit your application directly.
When was the Manager- Data Engineer job at KPMG Global Services posted?
This Manager- Data Engineer position at KPMG Global Services was posted on Jul 28, 2026. Apply as soon as possible — early applications are often reviewed first.
Manager- Data Engineer
KPMG Global Services
You'll be redirected to KPMG Global Services's official application page on oraclecloud.