Data Engineer - Clinical Decision Support Solutions

Job Description: Beckman Coulter is seeking a team member to join our Microbiology R&D Development Science functional team. In this role, you will develop and maintain end-to-end data and machine Learning pipelines for clinical and verification studies. We're looking for associates who thrive in a team-oriented, goal-focused environment. The Data Engineer for Beckman Coulter Diagnostics is responsible for the development and implementation of end-to-end Ops pipelines to support ML model deployment throughout the entire ML lifecycle. This position is part of the data science team located in Sacramento, California, and will be a hybrid role. The data engineer will be a part of the development science functional group and report to the data science manager. If you thrive in a cross-functional team and want to work to build a world-class biotechnology organization—read on. Responsibilities Collaborate with stakeholders to understand data requirements for ML, Data Science, and Analytics projects. Assemble large, complex data sets from disparate sources, writing code, scripts, and queries, as appropriate to efficiently extract, QC, clean, harmonize, and visualize Big Data sets. Write pipelines for optimal extraction, transformation, and loading of data from a wide variety of data sources using Python, SQL, Spark, and AWS 'big data' technologies. Develop and design data schemas to support Data Science team development needs. Identify, design, and implement continuous process improvements such as automating manual processes and optimizing data delivery. Design, develop, and maintain a dedicated ML inference pipeline on the AWS platform (SageMaker, EC2, etc.). Deployment of inference on a dedicated EC2 instance or Amazon SageMaker. Establish a data pipeline to store and maintain inference output results to track model performance and KPI benchmarks. Document data processes, write data management recommended procedures, and create training materials relating to data management best practices. Required Qualifications BS or MS in Computer Science, Computer Engineering, or equivalent experience. 5-7 years of Data and MLOps experience developing and deploying Data and ML pipelines. 5 years of experience deploying ML models via AWS SageMaker, AWS Bedrock. 5 years of programming and scripting experience utilizing Python, SQL, Spark. Deep knowledge of AWS core services such as RDS, S3, API Gateway, EC2/ECS, Lambda, etc. Hands-on experience with model monitoring, drift detection, and automated retraining processes. Hands-on experience with CI/CD pipeline implementation using tools like GitHub (Workflows and Actions), Docker, Kubernetes, Jenkins, Blue Ocean. Experience working in an Agile/Scrum based software development structure. 5 years of experience with data visualization and/or API development for data science users. #J-18808-Ljbffr