Data Engineering - Python, Azure & Gen AI

Required Qualifications & Experience: Technical Skills:

• Data Engineering: 10+ years of hands-on experience in building enterprise-scale data pipelines using PySpark, SQL, Python, Databricks.
• Cloud Architecture: Deep expertise in Azure (Azure Data Factory, ADLS Gen2, Synapse, Event Hubs).
• Data Platforms & Modeling: Strong experience with relational databases, Data Warehousing, Data Lakehouse paradigms, and Master Data Management (MDM) concepts.
• DevOps & Governance: Experience with Git, Azure DevOps, CI/CD pipelines, automated testing, and enterprise data governance frameworks.

• Proven ability to work in fast-paced, high-touch client environments, managing conflicting priorities and ambiguity.
• Exceptional verbal and written communication skills with the ability to convey complex technical concepts to non-technical business leaders.
• Demonstrated experience working within cross-functional POD structures and global delivery teams.
• Taking full accountability as the single point of ownership to lead and seamlessly execute end-to-end POD deliverables.
• Gather Real Time feedback, fix edge-case bugs, and deploy updates in days rather than waiting for quarterly core product cycles.
• Prior experience supporting major Life Sciences/Pharmaceutical enterprise environments (eg, R&D data fabric, clinical sample tracking, or commercial analytics).
• Hands-on experience to GenAI framework integrations (eg, LangChain, AutoGen, LlamaIndex) for data augmentation and automated RCA workflows.
• Professional certifications in Azure Data Engineer (DP-203), Databricks Certified Data Engineer.