- Salary
- $220k – $270k
- Location
- Rohnert Park, California · Rohnert Park, California, United States
- Department
- Corp IT
- Seniority
- Director
- Source
- Greenhouse
Description
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 500,000 patients worldwide.
The IT Director, Data Services and AI Enablement provides strategic leadership and operational oversight for Heartflow’s data engineering, systems integrations and automation, and AI enablement functions. This role leads a small team responsible for data infrastructure, enterprise integrations, automated workflows, and AI-enabled solutions that support organizational effectiveness.
This role drives the development and optimization of the enterprise data platform, delivering scalable, governed, high-quality data solutions that accelerate time-to-insight, improve reliability, and enable AI/ML and analytics through efficient, self-service access to analytics-ready data.
Data Infrastructure & Engineering
- Lead the design, development, and management of enterprise data infrastructure platform owning the end-to-end data lifecycle, including ingestion (batch, streaming, APIs), transformation (ETL/ELT), modeling, storage, integration, and delivery of data products.
- Oversee data pipelines, data modeling, and reporting solutions that support organizational decision-making while embedding governance, data quality, monitoring, and observability into workflows to reduce defects, latency, and operational inefficiencies.
- Ensure data accuracy, consistency, and accessibility across systems and stakeholders.
- Design and operationalize an enterprise semantic layer (e.g., Cube Cloud) to provide secure, context-rich, and standardized data access for AI applications and advanced analytics.
Analytics, AI Enablement, & Strategy
- Drive the company’s 'AI-readiness' by ensuring underlying data architectures are clean, structured, and highly available for advanced machine learning and generative AI workloads.
- Enable self-service analytics and data discoverability through tools like Tableau, semantic layers, and data catalogs while maintaining governance and data integrity.
- Lead the evaluation and implementation of AI-enabled tools and solutions that enhance decision-making and efficiency.
- Partner with business units to identify, evaluate, and prioritize high-value AI use cases.
- Partner with executive leadership to align data investments with corporate and digital transformation strategies.
Integration & Automation
- Direct the design and implementation of integrations across enterprise applications.
- Ensure integration reliability, scalability, and alignment with enterprise architecture.
- Lead the development of automated workflows that reduce manual processes and improve operational efficiency.
Governance & Continuous Improvements
- Support governance for data management, system integrations, and responsible use of data and AI.
- Establish and track key performance indicators related to data quality, adoption, and automation impact.
- Identify and implement improvements that enhance data reliability, efficiency, and user experience.
- Partner with stakeholders to translate business needs into data and reporting solutions.
- Partner with vendors and evaluate technologies aligned to enterprise data strategy and architecture.
- Drive FinOps initiatives and cost management strategies to optimize cloud infrastructure spend while maintaining high performance and scalability.
Educational Requirements & Work Experience
- Education: Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field. (A Master’s degree in a related field or Business Administration is highly preferred).
- Certifications (Preferred): Relevant cloud or data architecture certifications (e.g., AWS Certified Data Analytics, AWS Certified Solutions Architect, or equivalent governance certifications).
Required Experience
- Domain Expertise: 8+ years of progressive experience in data engineering, enterprise data architecture, or systems integration.
- Strategic Leadership: 4+ years of direct leadership experience, with a proven track record of translating complex enterprise business requirements into scalable data and analytics strategies.
- Modern Data Stack & Migrations: Demonstrated, hands-on leadership experience directing large-scale data architecture migrations. Must have deep familiarity with AWS infrastructure, cloud data warehousing (e.g., Redshift), and orchestration tools (e.g., Dagster).
- BI & Analytics Transformation: Proven experience managing enterprise business intelligence platforms and leading large BI migrations (e.g., transitioning from Domo to PowerBI).
- Enterprise Integration: Strong background in designing and managing complex integrations with core enterprise applications (e.g., Salesforce, NetSuite, ADP, Master Data Management).
Technical & AI Proficiencies
- AI Readiness & Semantic Layers: Understanding of modern semantic layers (e.g., Cube Cloud) and how to architect data governance to enable AI, machine learning, and advanced self-service analytics.
- Data Governance: Strong framework knowledge for establishing data quality, observability, and compliance across automated workflows.
- Industry Context (Preferred): Previous experience in MedTech, Healthcare, or Life Sciences, with an understanding of handling regulated or sensitive data ecosystems.
A reasonable estimate of the base salary compensation range is $220,000 to $270,000 per year, bonus, and equity. #LI-IB1 #LI-Hybrid
