Job Purpose Design and deliver data transformation pipelines, semantic models, and BI solutions that enable reliable, self-service analytics across the organization. The role bridges data engineering and business intelligence, owning the analytical data layer from source transformation through to governed, business-ready reporting assets.

Key Result Responsibilities

Design and maintain modular, well-tested ELT pipelines using tools such as dbt, Azure Data Factory, or equivalent orchestration frameworks.

Build and maintain the semantic/analytical data layer in Snowflake or Microsoft Fabric, including fact and dimension tables, conformed metrics, and reusable dbt models.

Develop production-grade Power BI reports and dashboards, including well-structured data models, DAX measures, and row-level security configurations.

Define and implement data quality rules, testing frameworks, and monitoring to ensure accuracy and consistency of analytics outputs.

Key Result

Responsibilities-Continued Collaborate with data analysts, business stakeholders, and data engineers to translate reporting requirements into robust, governed data assets.

Apply and enforce dimensional modelling principles (star schema, slowly changing dimensions) to support efficient BI consumption.

Work within Azure and Snowflake environments to manage datasets, optimize query performance, and control data access.

Maintain clear documentation for all pipelines, semantic models,



metric definitions, and report logic.

Participate actively in code reviews and contribute to improving team standards for analytics engineering.

Qualifications (Academic, Training, Languages) Bachelor's degree in Computer Science, Information Technology, Business Analytics, Statistics, or a related field. Fluent in English Language.

ITIL Certification is an advantage but not mandatory.

Strong SQL skills and solid working knowledge of Python for data transformation tasks.

Working knowledge of dbt for transformation layer development, including tests, documentation, and lineage.

Hands-on experience with Power BI: data modelling, DAX, report design, and workspace governance.

Demonstrable experience delivering Power BI solutions in a qualified setting.

Familiarity with Microsoft Fabric or Azure Data Factory for pipeline orchestration and data movement.

Solid understanding of dimensional modelling (star/snowflake schema, SCD types).

Understanding of BI governance principles: semantic model management, certified datasets, and access control in Power BI.

Work Experience

With 2–4 years of hands-on experience in analytics engineering, data engineering, or BI development.

Experience with Snowflake or Azure Synapse Analytics for data warehousing and query optimization.

Experience with Git-based version control and collaborative development workflows.

📌 Data Analytics Engineer I (Pune)
🏢 ISA
📍 Pune