Senior Analytics Engineer

Intercom•San Francisco, CA

15d•Hybrid

About The Position

Intercom is the AI Customer Service company on a mission to help businesses provide incredible customer experiences. Our AI agent Fin, the most advanced customer service AI agent on the market, lets businesses deliver always-on, impeccable customer service and ultimately transform their customer experiences for the better. Fin can also be combined with our Helpdesk to become a complete solution called the Intercom Customer Service Suite, which provides AI enhanced support for the more complex or high touch queries that require a human agent. Founded in 2011 and trusted by nearly 30,000 global businesses, Intercom is setting the new standard for customer service. Driven by our core values, we push boundaries, build with speed and intensity, and consistently deliver incredible value to our customers. What's the opportunity? The Analytics Engineering team provides all parts of Intercom’s business with the right set of accurate and up-to-date data tables that form the basis of all of our most important operational and strategic data-driven decisions. The team uses their deep knowledge of how GTM teams work to anticipate their data needs. They use their deep technical expertise to design the right data models and reliably turn these models into high-quality core data assets in our data warehouse. The team are experts at driving data model efficiencies and solving data quality issues at their root cause. This team is the glue between all of our Analyst teams and our Data Infrastructure teams. They speak everyone's language and make everything faster.

Requirements

You write advanced SQL with a preference for well-architected data models, optimized query performance, and clearly documented code
You’re familiar with the modern data stack. DBT and Snowflake experience are a big plus.
A growth mindset and eagerness to learn.
You exhibit great judgment and sharp business and product instincts that allow you to differentiate essential versus nice-to-have and to make good choices about trade-offs
You practice excellent communication skills, and you tailor explanations of technical concepts to a variety of audiences

Nice To Haves

Exposure to Apache Airflow or other DAG frameworks — we use Airflow to orchestrate and schedule all of our data workflows and transformations
Worked in Tableau, Looker, or similar visualization/business intelligence platform
Experience with operational tools and business systems such Google Analytics, Marketo, Salesforce, Segment, or Stripe
Familiarity with Python

Responsibilities

Data Platform Development: Design, build, and manage scalable data pipelines and ELT processes to support a robust, analytics-ready data platform.
Cross-functional Collaboration: Partner with engineering, analytics, and business teams to understand data needs and ensure accurate, insightful data solutions.
Data Strategy & Governance: Lead initiatives in data model development, data quality ownership, warehouse management, and production support for critical workflows.
Advanced Analytics & Insights: Conducted in-depth data analysis and built custom models to support strategic business decisions and performance measurement.
Automation & Optimization: Streamline data collection and reporting processes to reduce manual effort and improve efficiency.
Innovation in Data Infrastructure: Created scalable solutions like unified data pipelines and access control systems to meet evolving organizational needs.
Impact Measurement: Developed a strategy and technical pipeline for evaluating research output impact using citation metadata.