• Architect production-grade, fault-tolerant batch and real-time data pipelines within a compliance-governed AWS environment. • Lead data modeling (dimensional, SCD, data vault) and build API/file-based integrations with encryption and audit logging at every point. • Build CI/CD pipelines for data infrastructure with mandatory security scanning and approval gates. • Conduct code reviews, mentor engineers, and produce technical documentation including ADRs, runbooks, and compliance evidence packages.

• Build backend services, APIs, internal tools, lightweight UI/admin screens, automation, job runners, integrations, and customer-specific configuration around the data • Ingest, validate, transform, and document ERP, API, SQL, file, and cloud data; map product-defined KPIs to available sources and identify gaps or inconsistencies • Create validated, analysis-ready datasets with consistent schemas, reproducible transformations, and clear naming for reporting, APIs, product features, and customer-facing analytics • Deploy and operate reliable cloud solutions, preferably AWS, owning monitoring, alerts, failure handling, performance, cost, and operational reliability

• 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.