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