Key Responsibilities Design, develop, and maintain ETL/ELT pipelines using Microsoft Fabric. Work extensively with Fabric Notebooks, utilizing both SQL and Python (PySpark/Pandas) for data processing. Work extensively with custom Python based ETL/ELT pipelines Develop and optimize complex SQL queries for data extraction, transformation, and data warehousing. Build and manage data models and warehouse structures (fact/dimension tables, star schemas, Medallion architecture). Implement data ingestion pipelines from multiple sources including databases, APIs, and flat files. Leverage Microsoft Fabric components such as Dataflows Gen2, Pipelines, and OneLake. Integrate and prepare datasets for Power BI reporting and dashboards. Create interactive dashboards and reports using Power BI for business and leadership stakeholders. Create automated workflows using Power Automate. Collaborate with business stakeholders, especially within the pharmaceutical domain, to understand data requirements and KPIs. Ensure data quality, governance, and compliance, particularly in regulated pharma environments. Monitor, troubleshoot, and optimize pipeline performance. Maintain proper documentation, version control, and coding standards. Required Skills & Qualifications Core Technical Skills Hands-on experience with Microsoft Fabric for data engineering and analytics. Strong expertise in ETL/ELT processes and data pipeline development. Advanced proficiency in SQL (query optimization, joins, window functions, data transformations). Solid experience with Python scripting (Pandas, PySpark preferred). Experience working with Fabric Notebooks for SQL and Python. Strong understanding of data warehousing concepts and modeling techniques. Experience with Lakehouse architecture, Delta tables and Medallion architecture. Power Platform & BI Experience with Power BI (data modeling, DAX, report/dashboard development). Familiarity with Power Platform tools: Power Automate (Should have basic knowledge) Power Apps (good to have) Domain Expertise Prior experience in the Pharmaceutical / Life Sciences domain (highly preferred). Understanding of pharma data such as clinical, commercial, sales, or regulatory datasets. Awareness of data compliance and governance standards in pharma (e.g., GxP, HIPAA is a plus). Preferred Skills Experience with Azure ecosystem (Synapse, Data Factory, ADLS). Exposure to real-time or streaming data pipelines. Knowledge of API integrations and data ingestion frameworks. Familiarity with CI/CD pipelines and DevOps practices. Microsoft certifications (e.g., Fabric, Azure Data Engineer).