Manager, Analytics Engineering, Data & AI Foundations
New
H
HubSpotData & AI
Remote - USAFull-TimeManager
Salary154,800 - 247,700 USD per year
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Job Details
- Required Skills
- PythonSQLETLSnowflakeDevOpsData modelingdbtLookerGitHub
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
- A DevOps mindset characterized by automation, collaboration, continuous improvement, reliability, and frequent iteration
- 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
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
- 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
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