Senior Data Engineer

Tempus; United States; On-site; USD 150,000 - 190,000; Full time

Job summary

Tempus in the United States is building the Patient Evaluation Engine and seeks a Senior Data Engineer to own the data platform—pipelines, models, and services enabling real-time clinical evaluation across federated hospital networks.

You will primarily work with Google Cloud, TypeScript services, dbt on BigQuery, Pub/Sub workflows, and Terraform to deploy, secure, and scale data infrastructure in a HIPAA-regulated environment.

Skills

BigQuery

dbt

TypeScript

Google Cloud

Cloud infrastructure

Data pipelines

Tools

BigQuery

dbt

TypeScript

Terraform

Pub/Sub

Job description

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.

We are building the Patient Evaluation Engine: a high-scale, multi-modal healthcare platform where autonomous AI agents reason over clinical data to drive real-time clinical evaluation across federated networks of hospitals. We are looking for a Senior Data Engineer to build and own the data platform underneath it - the pipelines, models, and services that make EHR records, genomic results, and cardiovascular imaging discoverable, trustworthy, and usable by agents.

This is a data engineering role at its core, and it asks for two things beyond the usual scope. First, you should be a capable software engineer: the person who builds the pipeline here is the person who writes the service that exposes it, and you will regularly work in our TypeScript application and service code rather than handing that off. Second, you should know cloud infrastructure well, specifically Google Cloud - you will make real decisions about how this platform is deployed, scaled, secured, and paid for, not just what runs on it.

Our goal is to move beyond static data warehousing toward a dynamic, "agent-ready" data fabric that supports real-time clinical evaluation at enterprise scale, in a HIPAA-regulated environment. The platform is early and much of it is still being built, which is why we are looking for someone with high ownership and a strong self-starting instinct rather than someone waiting for a fully specified backlog.

What You'll Do

• Build the pipelines that feed the agents. Develop the systems that fetch, parse, and serve both structured and unstructured data in formats optimized for real-time inference - spanning clinical EHR records, high-throughput genomics (NGS), and cardiovascular imaging (Echo, Cath, ECG).
• Own the warehouse and its transformations. Build and maintain our dbt models on BigQuery, along with the tests, documentation, and SQL standards that keep a growing model layer trustworthy.
• Model the multi-modal patient record. Shape the data model across those domains, applying normalized and dimensional design as each one demands, and write the code that enforces it.
• Move data through event-driven services. Build and operate the Pub/Sub topics, subscriptions, and dead-letter handling that connect ingestion, evaluation, and result delivery, with the retry and idempotency behavior that reliability at scale requires.
• Make the data agent-ready. Build the data access patterns and metadata layers that let AI agents autonomously discover, query, and reason over structured and unstructured datasets, and the retrieval services those agents call.
• Write the software on top. Build the TypeScript services and APIs that handle agent input and output and coordinate specialized agents, meeting the platform's performance and scalability demands. You are expected to be comfortable in the application codebase, not only in the data layer.
• Own the infrastructure your platform runs on. Extend and operate the platform's Google Cloud footprint in Terraform - BigQuery datasets, Pub/Sub, Cloud SQL, Cloud Storage, Memorystore, Secret Manager, and the service-account and IAM model that governs access to clinical data.
• Scale across hospital networks. Build for federated networks of hospitals: multi-tenancy, high availability, and performance across hybrid on-prem and cloud environments built for sensitive health-system integrations.
• Guarantee ground truth. Implement automated solutions to monitor data quality and lineage with strict traceability back to source systems, ensuring "ground truth" for agentic evaluations.
• Instrument for trust. Build the observability, error tracking, and human-in-the-loop checkpoints that make automated clinical evaluation transparent and debuggable.
• Raise the standard around you. Partner with clinical, analytics, and platform engineering teams on data modeling standards, governance, and practices for maintaining data integrity in a HIPAA-regulated environment.

How You Work

We care about these as much as the technical checklist. High ownership. You own what you build all the way into production - you care whether it stays up, you chase root causes instead of symptoms, and you do not treat the deploy boundary as the end of your responsibility. Self-starter. The problem space is genuinely open. You are comfortable identifying the most valuable next thing and starting on it without a fully specified ticket, and you surface ambiguity early rather than stalling on it. Collaborative. You work directly with clinical, analytics, and platform engineering partners. You writ