[Hiring] Manager, Analytics Engineering @HubSpot

Role Description

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.

• 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.

Qualifications

• 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.

Benefits

• Flexible work environment with options for remote or in-office work.
• Support for career growth and development.
• Inclusive and diverse workplace culture.