Sr. Data Operations Analyst

Tempus AI•Chicago, IL

8d•Hybrid

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. As a Diagnostic Data Operations Analyst, you will serve as the technical data backbone of Tempus's diagnostics business unit. This role operates cross-functionally across Strategy & Operations, Business Intelligence, Commercial Systems, and AI Engineering — and has three core responsibilities: (1) developing and maintaining the data architecture that powers AI agents and cross-functional data products, (2) translating operational workflow requirements into technical specifications that Engineering and BI teams can execute against and (3) you will be the primary owner of dashboards and operational reports — responsible for building them, keeping them accurate, and ensuring they tell a clear story to leadership. Alongside that, you will define the data layer that makes agent building possible and partner cross-functionally to ensure data products are built on a stable, scalable foundation that supports both customer-facing and internal operations.

Requirements

Bachelor's degree in an analytical, computational, or healthcare-related field (e.g., Data Science, Bioinformatics, Computer Science, Biomedical Engineering, Public Health)
4–6 years of relevant experience in data analytics, data engineering, healthcare analytics, or clinical operations data
Deep proficiency in SQL and experience with data transformation tools (e.g., dbt); ability to write and optimize complex queries across multi-source schemas
Experience with data visualization tools (Looker strongly preferred; Tableau acceptable)
Demonstrated experience building or operationalizing AI/ML data pipelines, agent workflows, or automated data products
Excellent interpersonal and communication skills; proven ability to translate technical data requirements to non-technical stakeholders and business leadership
Comfort with ambiguity, ability to create structure in fast-moving environments, and strong instinct for prioritization when data access is incomplete
Proficient in Google Suite (Sheets, Docs, Slides) and Excel

Nice To Haves

Experience in healthcare diagnostics, oncology, or life sciences
Familiarity with CRM systems (Salesforce/SFDC) and clinical data systems (EMR, LIMS)
Experience with prompt engineering, LLM agent testing, or AI agent evaluation frameworks

Responsibilities

Build and own the operational data architecture: Serve as the technical lead in translating diagnostic business needs into operational data processes. Define data models, source-to-target mappings, and the structural logic that connects SFDC, OPUS, LIMS, Hub, and lab systems into unified, reliable data products.
Operationalize diagnostic data pipelines: Utilize SQL and data transformation tools to create and combine operational workflow data streams — including order, case, communication, and physician activity data — into formats that power dashboards, agents, and downstream analytics.
Lead data architecture for AI agent development: Partner with AI Engineering and Tempus One agent teams to define the data requirements for diagnostic workflow agents (e.g., order status, tissue request automation, cancellation workflows). Translate operational needs into BRDs and technical specifications that engineering teams can build from.
Support GenAI and agent lifecycle management: Assist in scoping, piloting, and ongoing monitoring of AI agents for diagnostic workflows. Define success metrics, establish ground truth evaluation frameworks, and manage the feedback loop between operations and the AI team post-launch.
Build cross-functional data products: Extend operational reporting into shared data products used by BI, Commercial Systems, and business leadership. Ensure that data is structured, documented, and accessible enough to serve as input for agent builds, executive reporting, and cross-team analytics.
Continuously improve the data pipeline: Evaluate and enhance existing data processing logic, implement new data sources, monitor for data drift post-agent launch, and partner with BI and Engineering to resolve data quality and latency issues.
Own reporting and dashboard development: Design, build, and maintain core Looker dashboards and operational reports — including physician health, workflow performance, case volume, and KPI tracking. You are the primary builder and maintainer; leadership should be able to rely on these products as their source of truth. This includes identifying KPIs with leadership, validating data accuracy, and iterating on dashboards as business needs evolve.
Partner cross-functionally: Engage with Business Intelligence, Commercial Systems/SFDC, AI Engineering, Product, and Internal Operations teams to identify data gaps, align on architecture decisions, and ensure data products are built on a stable, scalable foundation across the diagnostics business.