Sr Manager, Software Engineering

Tempus AI•Chicago, IL

1d•Onsite

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 Sr Manager, you will be the primary guardian of data reliability and a mentor to a high-performing engineering squad. You are a "Data Reliability Engineer" who ensures that our complex multi-modal datasets—spanning clinical records, genomics and radiology —are accurate, traceable, and ready for high-stakes applications like machine learning and clinical decision-making. In this leadership capacity, you will manage a small team of engineers leveraging cutting-edge AI-assisted development tools to accelerate delivery.

Requirements

Expert in the modern data engineering stack, specifically DBT, SQL, and Python.
Proven track record of leading or mentoring a small team of engineers in a fast-paced environment.
Hands-on experience with observability tools (e.g., Elementary, Grafana).
Proficient in leveraging AI coding assistants (Cursor, Claude, Copilot) to enhance team velocity.
Strong background in healthcare and next-generation sequencing (NGS) data.
Use data profiling and correlation analysis to solve complex data integrity problems.
Ability to translate technical data quality gaps into actionable insights for both internal developers and external stakeholders.

Responsibilities

Lead and manage a small, agile team of software engineers.
Conduct code reviews, provide technical guidance, and foster a culture of excellence in data quality and engineering best practices.
Architect and implement a comprehensive testing suite within DBT.
Design and enforce data quality gates in the CI/CD and ETL pipeline to ensure that only high-integrity data reaches production environments.
Perform exhaustive data profiling and deep-dive analyses using SQL and Python to evaluate completeness, identify hidden patterns, and resolve structural inconsistencies.
Collaborate directly with Product Managers and other Team Leads to develop and prioritize the product backlog.
Identify work estimates and refining complex requirements into actionable technical tasks.
Architect and implement robust observability frameworks using BigQuery, DBT, Elementary, and Grafana to create real-time monitors, alerts, and dashboards that track the health and lineage of our data ecosystem.
Champion the use of AI productivity tools—such as Cursor, Claude, and GitHub Copilot—to streamline development, automate testing, and refactor legacy data pipelines efficiently.
Define and report on key Data Quality KPIs (e.g., freshness, volume, and schema changes) using correlation and trend analysis to provide platform-wide transparency.
Manage an enterprise data model integrating multiple domains (Clinical, NGS, Radiology) across relational and NoSQL technologies.