Manager, Analytics Engineering, Data & AI Foundations
POS-33330
Manager, Analytics Engineering, Data & AI Foundations
HubSpot's mission is to Help Millions of Organizations Grow Better. HubSpot's Operations team is core to this goal by supporting the HubSpotters who drive growth and serve HubSpot's customers. Our goal in Operations is to embed advanced analytics and algorithmic intelligence into the DNA of decision-making across HubSpot. Flexible, well connected, well architected data are the backbone of our strategy, along with the Analytics Engineers who create that landscape.
In this role, you'll join the Data & AI Foundations team and lead Analytics Engineers responsible for the shared data foundations, platform capabilities, and AI enabled workflows that make HubSpot's Analytics Engineering organization more effective and scalable. You'll work across Analytics Engineering managers, analysts, analytics committees, and technical platform partners to understand workflows, identify friction, and turn feedback into a clear strategy and prioritized roadmap. You'll combine people leadership, technical judgment, change management, and hands on problem solving to help the organization adopt new ways of working.
In this role, you'll get to:
Lead and develop a team of Analytics Engineers, supporting career growth, hiring and retention, regular feedback, and alignment to the highest-impact priorities
Set the team's vision and operating model, translating broader Operations and Analytics Engineering goals into a focused roadmap and clear measures of success
Partner with Analytics Engineering managers to understand team workflows and identify opportunities to improve composable agents, semantic models, shared data assets, and developer tooling
Collaborate with analysts and analytics committees that use shared Analytics Engineering assets or build their own agents, incorporating their feedback into platform strategy and delivery
Work with technical leaders to review architecture and implementation approaches, establish scalable standards, and ensure solutions are robust, practical, and reusable
Prioritize a portfolio of cross functional initiatives, defining objectives, sequencing, responsibilities, and resource allocation based on impact and team capacity
Partner with AI Readiness delivery teams to deploy implementation resources against the highest priority work and remove blockers through execution
Own and evolve core platform assets, Analytics Engineering tooling, reusable patterns, and automation that raise the floor for every Analytics Engineer
Advance our composable agentic Analytics Engineering delivery system and support responsible adoption of AI-assisted workflows across the organization
Guide the development of semantic models and metric layers that serve as trusted, reusable foundations for analytics and AI consumption
Establish and champion standards for data modeling, testing, code review, documentation, observability, reliability, and cost management
Build enablement programs and cross-functional partnerships that help teams adopt modern tools and practices, while staying close enough to the work to contribute hands-on when it will accelerate progress
We are looking for someone with:
Experience leading and developing Analytics Engineers or similar technical data professionals, including hiring, coaching, performance management, and career development
Demonstrated ability to help define a vision and translate it into a practical strategy, roadmap, and operating model
Strong understanding of analysts' workflows and the role of shared data assets, tooling, and standards in improving quality and scale
Strong proficiency with SQL, data modeling, ETL, ELT, and modern data tools such as Snowflake, dbt, and Looker
Hands-on dbt experience, including scalable modeling patterns, testing, macros, and development workflows
Experience building or managing shared data platform assets, developer toolkits, internal frameworks, semantic models, or metric layers
Experience with AI assisted development and agentic workflows, such as Claude Code, composable skill based systems, AI agents, or MCP integrations
A track record of leading complex, cross-functional data initiatives from an ambiguous problem through production
Experience driving change and adoption across a large or complex organization, including influencing without direct authority and building accountability across teams
Strong prioritization and project management skills, including defining success criteria, sequencing work, and balancing impact with team capacity
A DevOps mindset characterized by automation, collaboration, continuous improvement, reliability, and frequent iteration based on user needs
Strong communication skills and the ability to distill technical decisions, trade-offs, and strategy into clear business terms
Experience working with globally distributed teams and version control tools such as GitHub Enterprise Cloud
Experience with Python is a plus, but not required