• Define and evolve the Accelerant Data Platform by gathering stakeholder requirements. • Identify opportunities to deliver value through data-driven solutions enabling Risk Exchange efficiency. • Collaborate with Product Managers, Engineers, and Designers to deliver high-quality solutions. • Develop and own roadmap for data ingestion and consumption with Outcome and Key Results (OKRs). • Communicate strategy and platform outcomes to various levels within the company.

Role Description The Head of Data owns the organization's overarching data strategy, foundational platforms, governance frameworks, and the analytics-to-AI value chain. Reporting directly to the Chief Executive Officer (CEO), this executive leader transforms the corporate data estate into a primary driver of decision advantage and bottom-line value. You will run a multi-disciplinary department across data engineering, business intelligence, data governance, and artificial intelligence/machine learning (AI/ML). Core Responsibilities - Strategic Leadership & Culture - Data Strategy Alignment: Establish the enterprise-wide data roadmap to explicitly match corporate strategic growth goals and deliver clear return on investment (ROI). - Data Literacy & Advocacy: Foster a mature, data-driven operational culture across all business functions by championing data literacy and product-thinking. - AI & Innovation: Lead data-driven innovation, building the production pipeline for advanced machine learning, predictive modeling, and generative AI initiatives. - Platform Architecture & Data Product Delivery - Data Lifecycles: Oversee end-to-end data lifecycle infrastructure, ensuring efficient ingestion, transformation, storage, and retrieval across modern cloud systems. - Business Analytics: Build high-utility analytics, dashboards, and reporting frameworks that senior business leaders rely on for commercial KPIs. - Product Infrastructure: Coordinate with engineering and product squads to seamlessly scale backend data warehouses and semantic access layers. - Governance, Privacy & Trust - Compliance & Lineage: Enforce rigorous enterprise data policies regarding lineage, security, privacy regulations (e.g., GDPR, CCPA), and automated data quality validation. - Access Control: Partner with the corporate security team to manage access control frameworks and secure data sharing rules for stakeholders. - People Management & Vendor Oversight - Team Scaling: Recruit, lead, and mentor a high-performing group of data engineers, data scientists, and business intelligence analysts. - Vendor Ecosystems: Negotiate and optimize contracts with critical third-party tooling, cloud providers, and specialized data migration vendors. Qualifications - Experience: 10+ years of progressive data management experience, with at least 4 years managing cross-functional, highly technical teams. - Strategic Acumen: Proven track record of linking data initiatives to commercial outcomes, generating measurable revenue increases or operational cost reductions. - Tech Stack Familiarity: Comprehensive technical fluency in cloud platforms (AWS, GCP, Snowflake, or Databricks), ETL/ELT pipelines, SQL, Python, and BI layers (Tableau, PowerBI). - Education: Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a highly quantitative field. - Communication: Elite written and verbal communication skills, with an explicit ability to distill complex analytical concepts for non-technical executives. Requirements - Experience guiding a company through rapid infrastructure changes, cloud migrations, or digital transformations. - Advanced understanding of AI ethics, risk management frameworks, and model deployment pipelines. Benefits - WORKING HOURS: REMOTE-FIRST & ASYNCHRONOUS - Async-First: We prioritize output over "hours in a seat." - EST Overlap: You must be available for a few hours of overlap with US-based EST working hours each day to facilitate team meetings and real-time support. How to Apply We are looking for a Head of Data, not a task-checker. To be considered, please submit your resume and a case study or detailed summary of a data production system, platform architecture, or AI/ML pipeline you built from the ground up in a startup or high-growth setting. Highlight the tools used, the bottlenecks solved, and the measurable increase in team efficiency or business ROI. Application Process Interested candidates should apply directly for consideration. Upon submitting your application, our hiring team will review your credentials and will notify you if you are selected to participate in the interview process. We appreciate your interest in IncubatorLab and kindly ask that you refrain from inquiries to our teams via LinkedIn. No third party or agency submissions will be accepted. Equal Opportunity Employer IncubatorLab is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We consider all qualified applicants for employment regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. If you have a disability or special need that requires accommodation, please feel free to contact us.

• Enable AI capabilities on the Data 4.0 platform using Snowflake Cortex • Build AI-ready datasets and semantic-ready data products • Support use cases such as metric intelligence, anomaly detection, and AI-assisted analysis • Help define reusable implementation patterns for Snowflake Cortex use cases • Collaborate with Analytics Engineering to expose trusted metrics and governed datasets for AI use cases • Support future AI/agentic capabilities such as monitoring agents, recommended actions, and personalized insights • Document implementation patterns, assumptions, and platform considerations • Ensure AI enablement aligns with data quality, access, and governance standards

• Design, build, and maintain robust, scalable data pipelines and ETL/ELT workflows • Develop and manage data warehousing solutions to support business intelligence and analytics needs • Ensure data quality, integrity, and availability across all data systems and sources • Collaborate with data scientists, analysts, and product teams to understand and fulfill data requirements • Optimize query performance and database architecture for speed and efficiency • Monitor, troubleshoot, and resolve data pipeline failures and incidents • Implement and enforce data governance, security, and compliance best practices • Document data models, processes, and infrastructure to support team knowledge sharing