<p style="text-align:left"><b>Passionate about precision medicine and advancing the healthcare industry?</b></p><p style="text-align:left">Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. <a href="https://himalayas.app/companies/tempus">Tempus</a>' 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.</p><h3>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.</h3><p>In this leadership capacity, you will manage a small team of engineers leveraging cutting-edge AI-assisted development tools to accelerate delivery.</p><h3>What You’ll Do</h3><ul><li><p>Team Leadership &amp; Mentorship: 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.</p></li><li><p>DBT Testing &amp; Quality Gates: 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.</p></li><li><p>Deep Data Analysis &amp; Profiling: Perform exhaustive data profiling and deep-dive analyses using SQL and Python to evaluate completeness, identify hidden patterns, and resolve structural inconsistencies.</p></li><li><p>Product Strategy &amp; Planning: Collaborate directly with Product Managers and other Team Leads to develop and prioritize the product backlog. You will be responsible for identifying work estimates and refining complex requirements into actionable technical tasks.</p></li><li><p>Modern Data Stack &amp; Observability: 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.</p></li><li><p>AI-Driven Development: 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.</p></li><li><p>Statistical Insights &amp; KPIs: 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.</p></li><li><p>Advanced Data Modeling: Manage an enterprise data model integrating multiple domains (Clinical, NGS, Radiology) across relational and NoSQL technologies.</p></li></ul><h3>