- 8+ years of experience in Data Warehousing, ETL, SQL Server and MSBI technologies, with strong hands-on expertise in SSIS, SSAS, SSRS, SQL/T-SQL and Data Modelling.
- Strong experience in designing, developing, maintaining, and troubleshooting enterprise Data Warehouse and ETL solutions, including complex SSIS packages, data integration workflows and large-volume data processing.
- Strong expertise in SQL Server and T-SQL, with hands-on experience developing complex Stored Procedures, Functions, Views, Triggers and optimized SQL queries, along with database and query performance tuning.
- Strong knowledge of SSAS and SSRS, including OLAP concepts, cubes, dimensions, measures, hierarchies, analytical models, enterprise reporting, datasets, parameters, and report development.
- Excellent understanding of logical and physical Data Modelling, including normalization/denormalization, ER modelling for OLTP and dimensional modelling for OLAP/Data Warehousing, with good knowledge of Kimball methodology, Fact/Dimension tables and Star/Snowflake schemas.
- Experience working with large-volume enterprise data, with strong analytical and troubleshooting skills to identify data quality, ETL, performance and production issues and implement sustainable solutions.
- Experience across the complete SDLC, including requirement analysis, technical design, development, testing, deployment, production support and maintenance, with the ability to deliver high-quality solutions within aggressive timelines.
- Strong experience working with multiple stakeholders, business teams and source-system teams to understand requirements, resolve data issues and deliver scalable data solutions.
- Ability to work effectively as an individual contributor as well as a technical team lead, including mentoring team members, conducting code reviews, providing technical guidance and driving technical deliverables.
- Good knowledge of JIRA, TFS, source control and deployment processes; understanding of CI/CD practices is an added advantage.
- Good exposure to Cloud Data Platforms and Cloud Data Warehousing, with hands-on or working knowledge of Google Cloud Platform (GCP), Google BigQuery and Google Cloud Storage, and exposure to Azure Data Factory/Azure and AWS data services being highly desirable.
- Experience or understanding of cloud data migration, modernization, cloud-based ETL and data integration is an advantage.
- Good understanding of AI, Machine Learning, Generative AI (GenAI)



and Large Language Models (LLMs), with exposure to applying AI capabilities within enterprise data and analytics environments.
- Understanding or hands-on exposure to Agentic AI / AI Agents, intelligent automation and AI-driven workflows, with the ability to identify opportunities where AI can improve data engineering, monitoring, reporting, troubleshooting and operational processes.
- Exposure to RAG (Retrieval-Augmented Generation), Google Cloud AI/ML/GenAI services and AI-assisted development tools such as GitHub Copilot or equivalent is desirable.
- Strong communication, stakeholder-management, analytical and problem-solving skills, with the ability to work effectively with onshore/offshore teams and across geographically distributed environments.
- Strong technical ownership and a continuous-learning mindset, with willingness to adopt and contribute to up-to-date technologies including Cloud, BigQuery, Data Engineering, AI, GenAI and Agentic AI while supporting the existing ADW ecosystem.

Roles & Responsibilities

- 8+ years of experience in Data Warehousing, ETL, SQL Server and MSBI technologies, with strong hands-on expertise in SSIS, SSAS, SSRS, SQL/T-SQL and Data Modelling.
- Strong experience in designing, developing, maintaining, and troubleshooting enterprise Data Warehouse and ETL solutions, including complex SSIS packages, data integration workflows and large-volume data processing.
- Strong expertise in SQL Server and T-SQL, with hands-on experience developing complex Stored Procedures, Functions, Views, Triggers and optimized SQL queries, along with database and query performance tuning.
- Strong knowledge of SSAS and SSRS, including OLAP concepts, cubes, dimensions, measures, hierarchies, analytical models, enterprise reporting, datasets, parameters, and report development.
- Excellent understanding of logical and physical Data Modelling, including normalization/denormalization, ER modelling for OLTP and dimensional modelling for OLAP/Data Warehousing, with good knowledge of Kimball methodology, Fact/Dimension tables and Star/Snowflake schemas.




- Experience working with large-volume enterprise data, with strong analytical and troubleshooting skills to identify data quality, ETL, performance and production issues and implement sustainable solutions.
- Experience across the complete SDLC, including requirement analysis, technical design, development, testing, deployment, production support and maintenance, with the ability to deliver high-quality solutions within aggressive timelines.
- Strong experience working with multiple stakeholders, business teams and source-system teams to understand requirements, resolve data issues and deliver scalable data solutions.
- Ability to work effectively as an individual contributor as well as a technical team lead, including mentoring team members, conducting code reviews, providing technical guidance and driving technical deliverables.
- Good knowledge of JIRA, TFS, source control and deployment processes; understanding of CI/CD practices is an added advantage.
- Good exposure to Cloud Data Platforms and Cloud Data Warehousing, with hands-on or working knowledge of Google Cloud Platform (GCP), Google BigQuery and Google Cloud Storage, and exposure to Azure Data Factory/Azure and AWS data services being highly desirable.
- Experience or understanding of cloud data migration, modernization, cloud-based ETL and data integration is an advantage.
- Good understanding of AI, Machine Learning, Generative AI (GenAI) and Large Language Models (LLMs), with exposure to applying AI capabilities within enterprise data and analytics environments.
- Understanding or hands-on exposure to Agentic AI / AI Agents, intelligent automation and AI-driven workflows, with the ability to identify opportunities where AI can improve data engineering, monitoring, reporting, troubleshooting and operational processes.
- Exposure to RAG (Retrieval-Augmented Generation), Google Cloud AI/ML/GenAI services and AI-assisted development tools such as GitHub Copilot or equivalent is desirable.
- Strong communication, stakeholder-management, analytical and problem-solving skills, with the ability to work effectively with onshore/offshore teams and across geographically distributed environments.
- Strong technical ownership and a continuous-learning mindset, with willingness to adopt and contribute to modern technologies including Cloud, BigQuery, Data Engineering, AI, GenAI and Agentic AI while supporting the existing ADW ecosystem.

📌 Senior Data Warehouse MSBI Engineer (Pune)
🏢 InfoVision
📍 Pune