Lead Product Performance Engineer

Axtria – An Overview: Axtria is a global provider of cloud software and data analytics to the Life Sciences industry. We help LifeSciences companies transform the product commercialization journey to drive sales growth and improvehealthcare outcomes for patients. We are acutely aware that our work impacts millions of patients and lead passionately to improve their lives. Since our founding in 2010, technology innovation has been our winning differentiation, and we continue to leapfrog competition with platforms that deploy Artificial Intelligence and Machine Learning. Our cloud-based platforms - Axtria DataMax, Axtria InsightsIQ, Axtria SalesIQ, and Axtria MarketingIQ - enable customers to efficiently manage data, leverage data science to deliver insights for sales and marketing planning and manage end-to-end commercial operations. With customers in over 30 countries, Axtria is one of the biggest global commercial solutions providers in the Life Sciences industry. We continue to win industry recognition for growth and are featured in some of the most aspirational lists - INC 5000, Deloitte FAST 500, NJBiz FAST 50, SmartCEO Future 50, Red Herring 100, and several other growth and technology awards. Axtria DataMAx™ is a global cloud-based data management product specifically designed for the life sciences industry. It facilitates the rapid integration of both structured and unstructured data sources, enabling accelerated and actionable business insights from trusted data This product is particularly useful for pharmaceutical companies looking to streamline their data processes and enhance decision-making capabilities. 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. 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. 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. 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 usecase for GenAI based data and 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. 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. 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