Senior Data Modeler

Tempus AI•Chicago, IL

About The Position

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. Tempus is executing on the mission to create the world’s largest, integrated dataset of multimodal healthcare and clinical data. At Tempus, products are owned and developed by small, autonomous teams composed of developers, designers, data scientists, clinicians, and product managers. You and your team set the goals, build the software, deploy the code, and contribute to a growing software platform that will make a lasting impact in the field of cardiovascular research and treatment. Tempus builds software as nimble as our teams. Our modern tech stack - Python, dbt and BigQuery on GCP - allows our teams to iterate rapidly and lead our industry in innovation. Our decentralized, microservice architecture and emphasis on automation allow us to deliver advanced solutions with confidence, and at scale.

Requirements

Domain knowledge in healthcare and informatics data.
Experience and knowledge of different data modeling techniques (e.g. 3NF, dimensional (star schema), and data vault).
Applied exceptional SQL skills in an enterprise data warehouse environment.
Knowledge of ETL/ELT and BI architectures, concepts and frameworks, and experience implementing them.
Understand and can clearly articulate the long-term impacts of key decisions between database technologies (relational, MPP, NoSQL) and have experience architecting solutions across multiple technologies.
Experience with data modeling tools like Erwin, Vertabelo or Lucidchart.

Nice To Haves

Experience with GCP architecture, particularly with BigQuery
Experience working with clinical and cardiovascular (echo, cath, ECG) data
Experience with Epic tables
Experience with HL7 data
Experience writing and debugging Python
Experience working with dbt
Implementing master, reference, or metadata management solutions

Responsibilities

Manage an enterprise data model in collaboration with clinicians, data scientists, engineers, product managers, and operators to integrate clinical data from source systems in multiple complex domains (clinical records, EHR data, ECG data, radiology, et al.).
Author and maintain entity-relationship diagrams, data dictionaries, API specs, and data translation documentation at multiple levels of abstraction (conceptual, logical, physical) and across multiple data store technologies (transactional, analytical).
Implement solutions to transform source data to a common data model
Collaborate with clinicians and data scientists to implement and validate feature definitions for clinical endpoints aligned with modeling priorities.
Implement solutions to proactively monitor data quality with traceability to source systems.
Evaluate completeness of source system data models and data by profiling partner data.
Advocate and educate engineering team members on data modeling rules, standards, and best practices.