To leverage expertise in data architecture and management to design, implement, and optimize a robust data warehousing platform for the pharmaceutical industry.
The goal is to ensure seamless integration of diverse data sources, maintain high standards of data quality and governance, and enable advanced analytics through the definition and management of semantic and common data layers.
Utilizing Axtria DataMAx and generative AI technologies, the aim is to accelerate business insights and support regulatory compliance, ultimately enhancing decision-making and operational efficiency.
Key Responsibilities
- Data Modeling: Design logical and physical data models to ensure effective data storage and retrieval.
- ETL Processes: Develop and optimize ETL processes to accurately and efficiently move data from various sources into the data warehouse.
- Infrastructure Design: Plan and implement the technical infrastructure, including hardware, software, and network components.
- Data Governance:



Ensure compliance with regulatory standards and implement data governance policies to maintain data quality and security.
- Performance Optimization: Continuously monitor and improve the performance of the data warehouse to handle large volumes of data and complex queries.
- Semantic Layer Definition: Define and manage the semantic layer architecture and technology stack to manage the lifecycle of semantic constructs including consumption into downstream systems.
- Common Data Layer Management: Integrate data from multiple sources into a centralized repository, ensuring consistency and accessibility.
- Deep expertise in architecting enterprise-grade software systems that are performant, scalable, resilient, and manageable. Architecting GenAI based systems is an added plus.
- Advanced Analytics: Enable advanced analytics and machine learning to identify patterns in genomic data, optimize clinical trials, and personalize medication.
- Generative AI: Should have worked with production-rea

📌 Data Architect (Gurugram)
🏢 Axtria
📍 Gurugram