Software Engineer II, Data Solutions

The Role

As a Senior Software Engineer II on the Data Products team, you will act as a technical leader in building the foundational infrastructure that powers Tempus’ clinical, research, and AI capabilities. The Data Products team provides a platform that supports the full lifecycle of data — from the secure ingestion of data to the cataloging and analysis of that data, powering complex algorithms and models.

You will play a pivotal role in architecting and driving technical strategy for services and APIs that allow bioinformatics scientists, ML and Agent developers to rapidly develop, deploy, and operate their solutions in a highly regulated, secure, and scalable environment. You will focus on data storage and governance, containerized compute orchestration, and forging new connections with the latest GenAI technologies to ensure our platform remains a robust foundation for cutting-edge technologies that help patients live longer, healthier lives.

Responsibilities

• Lead Technical Architecture: Drive the high-level design and architecture of distributed systems at scale, ensuring alignment with strategic goals for Tempus’s data management platform.
• System Design & Implementation: Design, implement, and test complex production backend web services in TypeScript, serving as a standard-bearer for code quality and system reliability.
• Data Pipelining: Build and maintain pipelines to load schema-defined data into the data warehouse using cloud storage and data warehousing technologies. Provision access to this data according to Tempus’s internal policies and regulatory obligations.
• Stakeholder & Product Leadership: Use exemplary stakeholder management skills to work closely with Product Managers to translate complex business requirements into technical roadmaps, ensuring clear communication and alignment with business goals.
• Operational Excellence: Take operational responsibility for platform systems in production, including on-call duties. You will not only troubleshoot complex technical issues but also proactively monitor systems to ensure reliability and performance optimization.
• Mentorship & Best Practices: Mentor other engineers on your team, fostering a culture of continuous learning. Along with your Engineering Lead, you will be responsible for defining and promoting best practices for software engineering, including testing strategies, documentation, and code reviews.
• Support & Enablement: Leverage your expertise to support cross-functional science, ML, and AI teams. You will act as a high-level escalation point to answer user questions and resolve development or production issues regarding data and compute workflows.

Qualifications

• Experience: 8+ years of experience in software or data engineering, with a strong focus on platform engineering, distributed data systems, or backend web services.
• Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent industry experience.
• Language Proficiency: Demonstrated success in writing production backend services in JavaScript/TypeScript, Python, or other similar languages, with a willingness to work primarily in a Node.js and Python environment.
• Advanced Cloud & Infrastructure Skills:
• Deep experience designing and running production systems on public cloud platforms (GCP preferred).
• Working knowledge of cloud object storage (GCS, S3), ETL services (Dataflow), and data warehousing (BigQuery).
• Data & Database Expertise: Strong familiarity with distributed, relational database design (GCP Cloud Spanner) and data modeling (JSON Schema, Avro, or similar), including schema migrations and enforcing data contracts.
• Technical Design: Proven track record of translating complex business requirements into scalable technical designs for distributed systems.

Preferred Qualifications

• Cloud Ecosystem: Experience with GCP services including GKE, Cloud Spanner, BigQuery, and Dataflow is a plus.
• DevOps Culture: Strong familiarity with CI/CD pipelines, immutable data patterns, and DevOps practices.
• Domain Knowledge: Experience with data cataloging, data lineage, or platforms supporting ML/GenAI and/or bioinformatics workflows.

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