Staff Software Engineer, Population Health (IC) at Navina

Ramat Gan, Tel-Aviv District, Israel -

Full Time

Start Date

Immediate

Expiry Date

05 Jul, 26

Salary

0.0

Posted On

06 Apr, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

React, Node.js, Python, Software Architecture, Data Pipelines, ETL, ELT, Data Warehousing, API Design, System Scalability, Technical Leadership, Full-stack Development, Code Review, Mentoring, Clinical Data Analysis, Performance Optimization

Industry

Hospitals and Health Care

Description

Navina is a fast-growing digital health SaaS company that’s on a mission to transform the way physicians interact with patient data. Thousands of clinicians across the United States already use Navina’s AI-powered solution that transforms complex and fragmented patient data into concise “patient portraits" and actionable clinical insights at the point of care. With Navina, physicians experience less burnout, reduce missed diagnoses, and can devote more time giving better care to their patients. Navina has been named one of the Top 100 AI companies globally by CB Insights and made the list of the Top 50 Digital Health startups. We are already working with industry-leading value-based organizations including Privia Health and Agilon. We are looking for a Tech Lead to serve as the technical "center point" for our Population Health engineering group. In this high-leverage, non-managerial role, you will float between multiple feature teams to solve the most complex architectural challenges, unblock technical debt, and ensure our systems scale as fast as our data does. You aren't just building features; you are building the engine that powers them. You will spend 70-80% of your time hands-on—coding, architecting, and creating the shared tools that make every other engineer in the group faster and more effective. Responsibilities Architectural Glue: Own the technical "contracts" (APIs) between teams to ensure seamless integration across the Pop Health ecosystem. Performance & Scalability: Optimize our engines and application layer to handle massive clinical datasets, ensuring our UI remains snappy and our backend is resilient. Data Pipeline Strategy: Lead the design and implementation of scalable BI data pipelines and robust ETL/ELT processes that transform complex clinical data into actionable insights. Force Multiplier: Identify friction points in the development lifecycle and build internal tools or libraries to increase engineering velocity. Technical Stewardship: Provide high-level code reviews, author RFCs for major architectural shifts, and mentor senior engineers through complex technical hurdles. Requirements 8+ Years of Engineering Excellence: Deep experience building and scaling large-scale, data-intensive web applications. Proven Technical Leadership: Experience as a Tech Lead or Architect, with a track record of driving complex projects to completion and steering technical roadmaps across multiple workstreams. Full-Stack Versatility: You are a true generalist, equally comfortable debugging a frontend rendering issue, optimizing a backend service, or tuning a complex data transformation. Full-Stack expertise: Expert-level knowledge of React, Node.js, and Python. Data Infrastructure Expert: Proven track record of building and managing modern data warehouses and high-throughput pipelines. You understand data modeling, concurrency, and how to optimize data flow from ingestion to visualization. Systems Thinker: You don't just fix bugs; you find the systemic cause and build a solution that prevents them for everyone. You don’t need to meet 100% of the requirements to be a great fit. We believe in hiring people, not just checklists, and we value potential as much as experience. If this role excites you, we’d love to see your application!

Responsibilities

You will serve as a technical lead for the Population Health group, architecting complex systems and ensuring seamless integration across the ecosystem. Additionally, you will optimize performance for massive clinical datasets and build internal tools to improve engineering velocity.