Ms Fabric

1 day ago

Delhi Delhi, India Qore WEB Full time

Role: MS Fabric

Remote

Budget : 1lpm

Experience : 3-8 yrs

Role Description

Key Responsibilities:
Design and implement scalable data solutions using Microsoft Fabric components:
OneLake, Data Lake, Lakehouse, Warehouse, and Real-Time Analytics

Build and manage end-to-end data pipelines integrating structured and unstructured data from multiple sources.

Integrate Microsoft Fabric with Power BI, Synapse Analytics, and Azure Data Factory to enable modern data analytics solutions.

Develop and maintain Power BI datasets, dashboards, and reports using data from Fabric Lakehouses or Warehouses.

Implement data governance, security, and compliance policies within the Microsoft Fabric ecosystem.

Collaborate with stakeholders for requirements gathering, data modeling, and performance tuning.

Leverage Azure DevOps / Git for version control, CI/CD pipelines, and deployment automation of data artifacts.

Monitor, troubleshoot, and optimize data flows and transformations for performance and reliability.

**Required Skills**:
3-8 years of experience in data engineering, BI development, or similar roles.

Strong hands-on experience with Microsoft Fabric ecosystem:OneLake, Data Lake, Lakehouse, Warehouse, Real-Time Analytics

Proficient in Power BI for interactive reporting and visualization.

Experience with Azure Synapse Analytics, ADF (Azure Data Factory), and related Azure services.

Good understanding of data modeling, SQL, T-SQL, and Spark/Delta Lake concepts.

Working knowledge of DevOps tools and CI/CD processes for data deployment (Azure DevOps preferred).

Familiarity with DataOps and version control practices for data solutions.

Preferred Qualifications:
Microsoft certifications (e.g., DP-203, PL-300, or Microsoft Fabric certifications) are a plus.

Experience with Python, Notebooks, or KQL for Real-Time Analytics is advantageous.

Knowledge of data governance tools (e.g., Microsoft Purview) is a plus.

Pay: ₹50,000.00 - ₹100,000.00 per year

Schedule:

- Day shift

Work Location: In person

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