Staff Data & AI Engineer

About the Role

Join our Data & RevOps team as a Staff Data & AI Engineer to revolutionize our sales operations through intelligent automation, real-time analytics, and custom AI applications. This exciting role combines analytics engineering, AI development, and sales force multiplication to drive significant revenue growth.

Responsibilities

• Instrument and operationalize signal detection by building data pipelines that capture intent signals from community engagement, product usage, and third-party tools.
• Build and maintain the data backbone for Go-To-Market Enablement (GTME) and AI solutions, ensuring clean data flow across HubSpot, data warehouses, and custom applications.
• Develop and maintain analytics infrastructure for sales operations, including data modeling, transformation pipelines (dbt, SQL), and real-time dashboards.
• Design and ship AI-powered applications using Python and LLMs to automate sales workflows, personalize outreach, and surface intelligent insights.
• Own the full stack of sales analytics, from raw data ingestion to semantic layers and end-user applications, building reliable and scalable systems.
• Prototype and iterate rapidly on AI solutions, including agentic systems, enrichment tools, lead scoring models, and conversational interfaces.
• Translate business requirements into technical solutions, collaborating closely with the GTME sales team to uncover data-driven opportunities.
• Develop predictive models and scoring systems using SQL, Python, and ML frameworks to optimize GTM motions.
• Create feedback loops and measurement frameworks to track the impact of GTME and enablement solutions.
• Ship internal tools that sales reps love, such as enrichment workflows, Slack bots, and AI research assistants.

Requirements

• Strong Python skills, comfortable building production applications, working with APIs, data manipulation, and AI/LLM libraries (OpenAI, LangChain).
• Advanced SQL and data modeling expertise, including designing dimensional models and building dbt projects.
• Proven experience shipping AI applications using LLM APIs, agent frameworks, or rapid prototyping tools.
• Proficiency with modern data tooling: dbt, data warehouses (Snowflake/BigQuery/Redshift), orchestration (Airflow/Dagster), and BI platforms (Looker/Tableau/Hex).
• Deep understanding of Go-To-Market (GTM) operations, including lead scoring, pipeline analytics, and sales workflows.
• Strong systems thinking, with a focus on scale, maintainability, reliability, data quality, monitoring, and error handling.
• Excellent communication skills, able to translate complex technical concepts into business value and collaborate effectively with non-technical stakeholders.
• CEFR Level C2 / ILR Level 5 proficiency in English (spoken, written, and reading).

Nice-to-Haves

• Experience in an Analytics Engineering, Data Engineering, or RevOps role at a modern SaaS company.
• A combination of deep technical skills and strong Go-To-Market intuition.