• Build experiences our customers love. Embed in the Video product org alongside PMs, designers, and engineers, using data to help shape and ship features athletes and their families actually use. You are a builder on the product team, not a reporting function next to it. • Strengthen and grow the data foundation. Make the models and pipelines we already have cleaner, more reliable, and more reusable, bring in new product, customer, and third-party signal we don't have today, and apply software discipline (version control, review, testing) throughout, so the team can trust the data and answer questions it currently can't. • Build experimentation into a system that scales. Stand up the data and analysis layer behind A/B tests so the team can design, run, and read experiments quickly and consistently, rather than rebuilding the plumbing each time. • Get instrumentation right at the source. Partner with product and engineering on event tracking and data contracts so the behavior we care about is captured accurately and completely from the start, then build the tests and monitoring that keep it that way. • Power product with live data APIs. Build and own data endpoints that feed real product experiences, from spec through production. • Surface insights and drive decisions. Turn the metrics that matter into clear, reliable insights and recommendations that move the business - building the dashboards and surfaces people trust to make decisions along the way. • Develop predictive and causal models. Develop predictive and causal models that move the business, from a pLTV model for revenue forecasting to a propensity model that targets discounts without cannibalizing revenue to causal-inference work that pinpoints which behaviors actually drive retention.

• 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