Enterprise Applications Developer 4

Duties: serve as a senior individual contributor leading and executing the design, development, and optimization of complex, enterprise-scale biomedical research and health data and technology solutions that operate on very large and highly complex multimodal datasets; support translational and biomedical research by delivering secure, high-performance, and scalable technical solutions that enable clinical research, population-scale analytics, and advanced computational science; serve as a senior technical contributor to solution development for designing, developing, and maintaining complex, enterprise-grade healthcare and research technology solutions; contribute as a senior technical contributor to data architecture, with hands-on responsibility for the development and evolution of large-scale, production-grade data engineering solutions supporting healthcare and research use cases; develop and operate across cloud-based platforms (e.g., Azure, AWS), Data Engineering Platforms (e.g. Databricks, Fabric), and high-performance computing (HPC) or supercomputing environments; design and optimize distributed data processing workflows operating at significant scale; lead performance tuning and optimization efforts for large-scale distributed data processing workloads by applying advanced techniques such as partitioning strategies, file layout optimization, execution tuning, memory management, and cost-aware compute configuration; troubleshooting; stay current with emerging technologies and best practices.

Requirements: Bachelor's degree in Computer Science, Data Science, Software Engineering, Data Engineering, Health Informatics, or a related field; 8 years of combined professional experience in software development and data engineering. That 8 years of experience must have included all of the following: designing, building, and optimizing complex, large-scale software systems and data platforms, including advanced data pipelines operating on very large datasets in healthcare and research environments; development using back-end programming languages (such as Python or Java) and SQL, and use of distributed computing frameworks such as Apache Spark; delivery of solutions on cloud-based data engineering platforms (e.g., Databricks) and integrating workflows across scientific high-performance computing (HPC) environments. (All experience may have been part-time, concurrent, and/or concurrent with education.) Requires successful completion of a background check.