Staff Analytics Engineer

HubSpot is an AI-powered customer platform dedicated to helping organizations grow better. The Staff Analytics Engineer will join the Data & AI Foundations team to design and build a modern data foundation that supports analytic and machine learning products, ultimately enhancing decision-making across the company.

Responsibilities:

• Build and optimize the performance of data pipelines and analytical tools for scale
• Own and evolve core platform assets, AE tooling, reusable patterns, and automation that raise the floor for every AE on the team
• Contribute to our composable agentic AE delivery system, a pipeline of AI-powered skills that automates the full delivery lifecycle from context to merged PR
• Design and maintain semantic models that serve as the trusted, reusable foundation for analytics and AI consumption across the organization
• Build internal AI agents and data-grounded tools, integrating RPC-based capabilities via MCP servers
• Design and implement cost strategies for shared data assets, pipelines, and compute usage
• Define and deploy scalable data ingestion, replication, and transfer patterns across systems
• Foster innovation with emerging technologies and by staying current with industry trends
• Guide professional development of the team through technical leadership
• Partner with stakeholders to solve business problems with technical solutions
• Build out scalable data models to analyze key parts of the HubSpot business
• Expand our suite of dbt patterns and macros to enable flexible and easily extensible data structures
• Drive data observability and pipeline reliability using tools like Monte Carlo
• Establish scalable patterns and standards for analytical application development in Hex
• Lead working groups, scope requirements, and usher projects through the entire lifecycle
• Maintain detailed documentation of data pipelines, processes, and best practices

Requirements:

• Expert knowledge of modern data tools (such as Snowflake, dbt, and Looker)
• Extensive proficiency in SQL, data modeling, ETL, ELT, and data transformation
• Deep dbt expertise including advanced modeling patterns, macros, and package development
• Experience developing slowly changing dimension (SCD) tables from multiple sources
• Successful experience leading complex, cross-functional data initiatives from ambiguous problem to production
• Experience building or maintaining shared data platform assets, developer toolkits, or internal frameworks
• Experience designing semantic models or metric layers for analytics or AI consumption
• Proficiency with AI-assisted development tools such as Claude Code, including agentic pipeline design and composable, skill-based workflows
• Comfort building AI agents and integrating external tools and services via MCP servers
• Experience utilizing version control tools (such as GitHub Enterprise Cloud)
• A DevOps mindset characterized by automation, collaboration, continuous improvement, and a hyperfocus on user needs and frequent iteration
• Strong communication skills and ability to distill technical solutions into business terms
• Experience with Python is a plus, but not required