Data Transformation Engineer
Do you want to be part of an inclusive team that works to develop innovative therapies for patients? Every day, we are driven to develop and deliver innovative and effective new medicines to patients and physicians. If you want to be part of this exciting work, you belong at Astellas!
Astellas Pharma Inc. is a pharmaceutical company conducting business in more than 70 countries around the world. We are committed to turning innovative science into medical solutions that bring value and hope to patients and their families. Keeping our focus on addressing unmet medical needs and conducting our business with ethics and integrity enables us to improve the health of people throughout the world. For more information on Astellas, please visit our website at www.astellas.com.
This is a remote position and is based in India. Remote work from certain states may be permitted in accordance with Astellas’ Responsible Flexibility Guidelines. Candidates interested in remote work are encouraged to apply.
Purpose and Scope:
The Data Transformation Engineer role serves the purpose of fostering innovation and operational efficiency through the facilitation of data-driven decision-making processes, expediting drug discovery initiatives, and bolstering regulatory compliance efforts. This role involves designing and implementing customized data architecture solutions tailored to the complexities of life sciences research and development. It also includes supervising the ingestion and management of various data sources, such as clinical trials data, operational data, genomic data, and real-world evidence.
Through close collaboration with research scientists, statisticians, clinicians, regulatory experts, and DigitalX professionals, this role establishes and upholds robust data architecture frameworks that align harmoniously with business objectives, regulatory mandates, and industry standards. Expertise in Data Engineering, Data Modelling, Business Intelligence (BI) technologies, and adeptly managing governance processes is essential for maintaining the integrity, security, and accessibility of data assets. This role holds a strategic position in advancing the life sciences company's mission by using data to drive scientific progress, improve patient outcomes, and efficiently and securely introduce ground-breaking therapies to the market.
Essential Job Responsibilities:
Develop and maintain comprehensive data transformation strategies tailored to the unique needs of Clinical and Operational data management. Design scalable, efficient, and secure ETL, data warehouses, and data lakes to store and manage clinical data effectively. Define and implement data models, schemas, and integration frameworks to ensure the integrity and accessibility of clinical data. Perform all data engineering tasks and implement data pipelines and ETL processes to ingest, transform, and load clinical data from various sources into the data infrastructure. Optimize data workflows through ELT processes and other strategies to ensure smooth operation and performance. Develop APIs, interfaces, and middleware solutions for seamless communication between clinical software systems and databases. Implement security controls, encryption mechanisms, and access management policies to protect sensitive data from unauthorized access, breaches, and cyber threats. Monitor compliance with healthcare regulations such as HIPAA, GDPR, and other data privacy laws, and implement measures to address potential risks and vulnerabilities. Document data ingestion designs, technical specifications, and operational procedures to facilitate knowledge transfer and support ongoing maintenance and enhancements. Communicate effectively with stakeholders to ensure alignment of engineering solutions with organizational goals and objectives. Stay abreast of emerging technologies, trends, and best practices in clinical data management, data ingestion and transformation. Identify opportunities for process improvement, automation, and innovation to enhance the efficiency and effectiveness of data management practices.