Staff Analytics Engineer

In this role, you’ll get to:

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

We are looking for someone with:

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