Senior Data Architect

Job Description
AbbVie’s Genomics Research Center (GRC) is a center of excellence for genetics and genomics that supports the R&
D pipeline from Discovery through Development. The GRC goal is to develop world class genetics and genomics research focused on finding the right therapeutic targets, helping AbbVie scientists better understand human disease biology, understanding the behavior and response to our drugs in clinical trials, and improving production of our biological drugs.
We are seeking a talented and highly motivated Senior Data Architect to join the GRC’s Bioinformatics Engineering (BE) team. This position will support the GRC’s strategy of maximizing the value of our data through the development of scalable data storage and management solutions. Strong communication and teamwork skills are essential as the candidate is expected to operate in a high-paced and multi-disciplined environment across geographic locations.
Responsibilities:
Design and develop scalable and efficient omics data storage platforms to accommodate large-scale bioinformatics data.
Collaborate with bioinformatics engineers and scientists to understand data requirements and develop loading processes for newly generated omics data.
Build robust and secure APIs to facilitate data access, retrieval, and integration across different bioinformatics applications.
Optimize the performance of data storage and retrieval operations to ensure quick and accurate access to omics data.
Implement data governance and security measures to maintain data integrity and assure stakeholder access.
Stay up-to-date with emerging technologies and industry trends in bioinformatics and data architecture to drive innovation and improve data management processes.
Provide technical leadership and guidance to junior data architects and developers in the team.
Collaborate with other Bioinformatics Engineering sub teams to identify and address data integration and interoperability challenges.
Conduct performance analysis and troubleshooting to identify bottlenecks and implement solutions for improved system performance.
Collaborate with data scientists and analysts to develop data models and structures that enable effective data analysis and interpretation.