Passionate about precision medicine and advancing the healthcare industry?
Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time.
As a Data Platform Engineer at Tempus, you will architect and implement cloud-native data pipelines and infrastructure to enable analytics and machine learning on Tempus’s rich clinical, molecular, and imaging datasets.
Why we’re looking for you:
You know what it takes to build and run resilient data pipelines in production and have experience implementing ETL/ELT to load a multi-terabyte enterprise data warehouse.
You have implemented analytics applications using multiple database technologies, such as relational, multidimensional (OLAP), key-value, document, or graph.
You value the importance of defining data contracts, and have experience writing specifications including REST APIs.
You write code to transform data between data models and formats, preferably in Python or PySpark.
You've worked in agile environments and are comfortable iterating quickly.
Bonus points for:
Experience moving trained machine learning models into production data pipelines.
Healthcare domain knowledge and experience with healthcare transmission formats (e.g. FHIR, HL7, ANSI X12) and data models (e.g OMOP).
Expert knowledge of relational database modeling concepts, SQL skills, proficiency in query performance tuning, and desire to share knowledge with others.
Experience building cloud-native applications and supporting technologies / patterns / practices including: AWS, Docker, CI/CD, DevOps, and microservices.