• Build and optimize the performance of data pipelines and analytical tools for scale • Own and evolve core platform assets, AE tooling, reusable patterns, and automation that raise the floor for every AE on the team • Contribute to our composable agentic AE delivery system, a pipeline of AI-powered skills that automates the full delivery lifecycle from context to merged PR • Design and maintain semantic models that serve as the trusted, reusable foundation for analytics and AI consumption across the organization • Build internal AI agents and data-grounded tools, integrating RPC-based capabilities via MCP servers • Design and implement cost strategies for shared data assets, pipelines, and compute usage • Define and deploy scalable data ingestion, replication, and transfer patterns across systems • Foster innovation with emerging technologies and by staying current with industry trends • Guide professional development of the team through technical leadership • Partner with stakeholders to solve business problems with technical solutions • Build out scalable data models to analyze key parts of the HubSpot business • Expand our suite of dbt patterns and macros to enable flexible and easily extensible data structures • Drive data observability and pipeline reliability using tools like Monte Carlo • Establish scalable patterns and standards for analytical application development in Hex • Lead working groups, scope requirements, and usher projects through the entire lifecycle • Maintain detailed documentation of data pipelines, processes, and best practices

• Set the direction Own the long term direction of the Intelligence offering, spanning data models, analytics, and customer facing insights. • Partner with Product to build a roadmap for our data science offerings. • Provide insights and what-if analysis tooling for our C-Suite customers. • Establish the architectural principles, analytical standards, and ways of working that allow Intelligence to scale safely and credibly. • Design and evolve the data architecture and canonical data model that underpins Intelligence, building on an existing warehouse. • Develop and ship data science work: predictive signals, pattern detection, automated insights: that genuinely moves the needle for customers. • Design and deliver dashboards that customers trust and rely on. • Own analytics engineering end to end: modelling data, optimizing queries, versioning work, and reviewing changes before they reach customers. • Introduce tooling and processes that speed up development while improving quality and consistency. • Work hand in hand with Engineering to ship Intelligence features into the product. • Engage directly with customers to understand reporting needs, explain insights, and validate value. • Support Commercial and Operations teams with insights that underpin upsell, renewal, and confident decision making.

• Own the architecture and governance of Thistle’s end-to-end data infrastructure, ensuring scalability, reliability, and adherence to best practices • Lead the design and implementation of our core data models and pipelines (ETL/ELT), enabling advanced analytics, reporting, and experimentation • Drive cross-functional alignment on data definitions, metrics, and source-of-truth models to ensure consistency and clarity across teams • Develop and mentor analytics engineers and analysts, elevating technical standards and fostering a data-driven mindset throughout the company • Serve as a strategic partner to senior leadership by identifying opportunities for data to unlock business value and advising on high-impact initiatives • Continuously optimize the performance of our data warehouse (e.g., Snowflake, BigQuery, or Redshift), workflows, and query efficiency • Champion best-in-class tooling and practices, including dbt, Airflow, version control, testing frameworks, and documentation standards • Support self-serve analytics by building scalable, intuitive data products, dashboards, and exploration tools tailored to business stakeholders.

• End-to-end ownership of data platform and BI layer • Contribute to a zero-waste future by reducing food waste and emissions • Evolve vendor-built BI solution into a modern data platform • Design and operate scalable pipelines and lakehouse architecture • Build and maintain customer-facing dashboards and embedded analytics • Define standards, data contracts, and architecture principles