Director of IT, Data Services and AI Enablement

Heartflow•Rohnert Park, CA

2d•$220,000 - $270,000•Hybrid

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, or a related technical field.
8+ years of progressive experience in data engineering, enterprise data architecture, or systems integration.
4+ years of direct leadership experience, with a proven track record of translating complex enterprise business requirements into scalable data and analytics strategies.
Demonstrated, hands-on leadership experience directing large-scale data architecture migrations.
Deep familiarity with AWS infrastructure, cloud data warehousing (e.g., Redshift), and orchestration tools (e.g., Dagster).
Proven experience managing enterprise business intelligence platforms and leading large BI migrations (e.g., transitioning from Domo to PowerBI).
Strong background in designing and managing complex integrations with core enterprise applications (e.g., Salesforce, NetSuite, ADP, Master Data Management).
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.
Strong framework knowledge for establishing data quality, observability, and compliance across automated workflows.

Nice To Haves

A Master’s degree in a related field or Business Administration is highly preferred.
Relevant cloud or data architecture certifications (e.g., AWS Certified Data Analytics, AWS Certified Solutions Architect, or equivalent governance certifications).
Previous experience in MedTech, Healthcare, or Life Sciences, with an understanding of handling regulated or sensitive data ecosystems.

Responsibilities

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.
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.
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.
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.