Data Analyst

Heartflow is a medical technology company advancing the diagnosis and management of coronary artery disease, the #1 cause of death worldwide, using cutting-edge technology. The flagship product—an AI-driven, non-invasive cardiac test supported by the ACC/AHA Chest Pain Guidelines called the Heartflow FFRCT Analysis—provides a color-coded, 3D model of a patient’s coronary arteries indicating the impact blockages have on blood flow to the heart. Heartflow is the first AI-driven non-invasive integrated heart care solution across the CCTA pathway that helps clinicians identify stenoses in the coronary arteries (RoadMap™Analysis), assess coronary blood flow (FFRCT Analysis), and characterize and quantify coronary atherosclerosis (Plaque Analysis). Our pipeline of products is growing and so is our team; join us in helping to revolutionize precision heartcare.

Heartflow is a publicly traded company (HTFL) that has received international recognition for exceptional strides in healthcare innovation, is supported by medical societies around the world, cleared for use in the US, UK, Europe, Japan and Canada, and has been used for more than 750,000 patients worldwide. The Data Analyst builds and maintains the business-facing layer of HeartFlow’s data platform — analytical applications, the reporting and visualization architecture, and the semantic model definitions that serve them. This is a hands-on technical role combining application development with business intelligence architecture: work product includes code that is version controlled, peer reviewed, and deployed through continuous integration, as well as the dashboards, reports, and interfaces through which the business consumes it. The role builds on data warehoused in Amazon Redshift, contributing to the semantic model, the reporting and visualization architecture, analytical applications, and the data quality validation and access governance around them. You will work directly with Sales, Marketing, Finance, Production, Compliance, Customer Success, and Medical Education, translating business questions into engineered, reusable data products rather than one-off deliverables. The role spans both engineering and business fluency. The same person who writes the application code is expected to sit with the stakeholder who will use it, understand the decision it supports, and stay with it through adoption and iteration. Work ranges from a governed dashboard consumed by sales leadership to a purpose-built application that replaces a manual process outright. Job

Responsibilities:
Analytical Application Development:

Design, build, and maintain business-facing analytical applications and interfaces using Python and JavaScript. Own the development lifecycle including requirements, design, implementation, testing, deployment, and ongoing maintenance. Business Intelligence & Reporting:

Develop and maintain the business-facing reporting and visualization layer within Tableau — calculated field and parameter logic, dashboard design, and publishing and permissions management — building on the data sources and extract strategy designed by the Analytics Engineering function. Design dynamic and fluid visualizations for a wide range of stakeholder groups across all user levels, and create relevant measures and KPIs covering commercial performance, business performance indicators, pipeline management, and ad hoc strategic initiatives. Semantic Model Development:

Author and extend Cube semantic model definitions as version-controlled code, submitted through pull request review. Implement measures, dimensions, and views consistent with the function’s modeling conventions so that business metric definitions remain centrally governed and consistently consumed. CRM Data & Reporting:

Design and maintain advanced reporting and analysis on Salesforce.com (SFDC) data, applying working knowledge of SFDC reporting structure, object-field relationships, and the varied dynamics of the system. Support business users in the effective use of CRM reporting. Engineering Practice:

Work within the function’s engineering operating model: Git-based version control, branching strategy, pull request authorship and review, branch protection, and CI/CD through GitHub Actions. Participate in code review as both author and reviewer. Data Quality & Stewardship:

Analyze and curate varied sources of data to address key business priorities. Maintain strong data integrity including routine QC and validation across the reporting and semantic suite, contribute to automated testing and data quality validation, and identify and escalate data defects in CRM and other source data consumed by function-owned systems. Data Governance:

Administer role-based access control and row-level security for function-owned reporting and application