Data Architect

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 efficient 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-ready use case for GenAI based data.
• Stakeholder Engagement: Work closely with business stakeholders to understand their data needs and translate them into technical solutions.
• Cross-Functional Collaboration: Collaborate with IT, data scientists, and business analysts to ensure the data warehouse supports various analytical and operational needs.

Qualifications

• Proven experience in data architecture and data warehousing, preferably in the pharmaceutical industry.
• Strong knowledge of data modeling, ETL processes, and infrastructure design.
• Experience with data governance and regulatory compliance in the life sciences sector.
• Proficiency in using Axtria DataMAx or similar data management products.
• Excellent analytical and problem-solving skills.
• Strong communication and collaboration skills.

Preferred Skills

• Familiarity with advanced analytics and machine learning techniques.
• Experience in managing semantic and common data layers.
• Knowledge of FDA guidelines, HIPAA regulations, and other relevant regulatory standards.
• Experience with generative AI technologies and their application in data warehousing.