- 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.

- Solid 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.

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