Manager, Analytics Engineering, Data & AI Foundations at HubSpot

Job description

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 At HubSpot, we value both flexibility and connection. Whether you’re a Remote employee or work from the Office, we want you to start your journey here by building strong connections with your team and peers. If you are joining our Engineering team, you will be required to attend a regional HubSpot office for in-person onboarding. If you join our broader Product team, you’ll also attend other in-person events, such as your Product Group Summit and other gatherings, to continue building on those connections. If you require an accommodation due to travel limitations or other reasons, please inform your recruiter during the hiring process. We are committed to supporting candidates who may need alternative arrangements About HubSpot HubSpot (NYSE: HUBS) is an AI-powered customer platform with all the software, integrations, and resources customers need to connect marketing, sales, and service. HubSpot's connected platform enables businesses to grow faster by focusing on what matters most: customers. At HubSpot, bold is our baseline. Our employees around the globe move fast, stay customer-obsessed, and win together. Our culture is grounded in four commitments: Solve for the Customer, Be Bold, Learn Fast, Align, Adapt & Go!, and Deliver with HEART. These commitments shape how we work, lead, and grow. We’re building a company where people can do their best work . We focus on brilliant work, not badge swipes. By combining clarity, ownership, and trust, we create space for big thinking and meaningful progress. And we know that when our employees grow, our customers do too. Recognized globally for our award-winning culture by Comparably, Glassdoor, Fortune, and more, HubSpot is headquartered in Cambridge, MA, with employees and offices around the world. Explore more: HubSpot Careers Life at HubSpot on Instagram If you need accommodations or assistance due to a disability, please reach out to us using this form . Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Germany Applicants: (m/f/d) - link to HubSpot's Career Diversity page here . India Applicants: link to HubSpot India's equal opportunity policy here . HubSpot may use AI to help screen or assess candidates, but all hiring decisions are always human. More information can be found here . By submitting your application, you agree that HubSpot may collect your personal data for recruiting, global organization planning, and related purposes. We may use CLEAR ID Verification during the hiring process to confirm your identity and help maintain a safe, secure, and trusted experience for all candidates. Refer to HubSpot's Recruiting Privacy Notice for details on data processing and your rights.

Requirements

Must-haves

Manager, Analytics Engineering, Data & AI Foundations
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
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

Nice-to-haves

Experience with Python is a plus, but not required

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