Data Engineer

3 weeks ago


West Sacramento, California, United States Omni Inclusive Full time
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 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 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, 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 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
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