Manager, Analytics Engineering, Data & AI Foundations
About The Position
HubSpot's Operations team is core to its mission of helping millions of organizations grow better by supporting the HubSpotters who drive growth and serve HubSpot's customers. The team's goal is to embed advanced analytics and algorithmic intelligence into the DNA of decision-making across HubSpot, with flexible, well-connected, and well-architected data as the backbone, supported by Analytics Engineers. In this role, you will join the Data & AI Foundations team and lead Analytics Engineers responsible for shared data foundations, platform capabilities, and AI-enabled workflows to enhance the effectiveness and scalability of the Analytics Engineering organization. You will collaborate with Analytics Engineering managers, analysts, analytics committees, and technical platform partners to understand workflows, identify challenges, and develop a clear strategy and prioritized roadmap. This role requires a combination of people leadership, technical judgment, change management, and hands-on problem-solving to guide the organization in adopting new ways of working.
Requirements
- 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.
Nice To Haves
- Experience with Python is a plus, but not required.
Responsibilities
- 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.
Benefits
- Flexible, well connected, well architected data are the backbone of our strategy, along with the Analytics Engineers who create that landscape.
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What This Job Offers
Job Type
Full-time
Career Level
Manager
Education Level
No Education Listed
