Microsoft Developer- Fabric

About Sonata Software

In today's market, we observe a distinct duality in technology adoption. On one front, clients are keenly focused on cost containment, while on the other, there is a strong drive to modernize their digital storefronts, aiming to appeal to both consumers and B2B customers alike. As a leading Modernization Engineering company, we aim to deliver modernization-driven hypergrowth for our clients based on the deep differentiation we have created in Modernization Engineering, powered by our Lightening suite and 16-step Platformation playbook. In addition, we bring agility and systems thinking to accelerate time to market for our clients.Headquartered in Bengaluru, India, Sonata Software has a strong global presence, with strategic operations spanning across key regions such as the US, UK, Europe, APAC, and ANZ. We are a trusted partner of world-leading companies in TMT (Telecom, Media, and Technology), Retail & CPG, Manufacturing, BFSI (Banking, Financial Services and Insurance), and HLS (Healthcare and Lifesciences). Our bouquet of Modernization Engineering services cuts across Cloud, Data, Dynamics, Contact Centers, and around newer technologies like Ge Job Title : Data Science Engineer

Experience : 4–8 years

Role Summary : Role Overview :

The Microsoft Fabric Developer will be responsible for designing, developing, and maintaining end-to-end data and analytics solutions using the Microsoft Fabric platform. The role involves working across Data Engineering, Data Science, and Business Intelligence components within Fabric — leveraging Data Factory, Lakehouse, Data Engineering, Data Science, and Power BI experiences to deliver scalable, governed, and business-ready insights.

This position requires strong technical expertise in Microsoft Fabric architecture, Power BI, and Azure data services, coupled with the ability to translate business needs into performant, automated data pipelines and analytical models.

Key Responsibilities :

Data Ingestion & Integration

Design and implement data ingestion pipelines using Microsoft Fabric Data Factory .

2. Data Engineering & Modeling

Build Lakehouses, Warehouses , and Data Models using Fabric Data Engineering capabilities.

3. Business Intelligence & Visualization

Develop semantic models and interactive dashboards using Power BI within Fabric .

4. Collaboration & Automation

Collaborate with Data Scientists , Analysts , and Business SMEs to support ML model training and deployment pipelines within Fabric.

5. Governance & Best Practices

Implement data security, RBAC, sensitivity labels , and data sharing policies in Fabric

Required Skills & Experience :

4–8 years of experience in data engineering, BI, or analytics , with at least 1 year in Microsoft Fabric .

Strong proficiency in Microsoft Fabric components , including :

Data Factory, Lakehouse, Data Engineering, Data Science, Warehouse, Power BI, OneLake.

Advanced skills in SQL , PySpark , and Data Modeling .

Experience with Power BI datasets, DAX, and data visualization best practices .

Familiarity with Microsoft Purview for governance and Entra ID (Azure AD) for access control.

Understanding of ETL / ELT pipelines , CI / CD for data pipelines , and Azure DevOps integration.

Exposure to Azure Synapse , Databricks , or Azure Data Lake migration to Fabric.

Good-to-Have Skills :

Experience integrating Microsoft Fabric with Copilot and GenAI-based insights .

Knowledge of Python, Notebooks , or ML model deployment within Fabric Data Science experience.

Understanding of RAG pipelines , AI-driven analytics , or Semantic Kernel integration.

Certification : Microsoft Certified : Fabric Analytics Engineer Associate (DP-600) .

Exposure to D365 data sources , Power Platform , or Azure Data Factory (legacy) .

Education :

Bachelor’s or master’s degree in computer science, Data Engineering, Information Systems, or a related field.

Soft Skills :

Strong analytical and problem-solving skills.

Excellent collaboration and communication abilities.

Ability to translate business needs into data-driven outcomes.

Focus on performance optimization, governance, and automation.