Analytics Engineer, Conversational Analytics

Role Overview

We are looking for an Analytics Engineer who will supercharge the organization by more fully unlocking Conversational Analytics. You thrive in a startup setting, where every day is different and the problems that you’re tackling don’t have established solutions.

The role has two primary focuses:

1. Building data models/semantic models, documentation, context, and evaluations to strengthen our LLMs’ ability to consistently achieve the correct answers
2. Training non-data stakeholders to more efficiently and effectively interact with our data stack

What You'll Do

• For the first 2-3 months, embed with a stakeholder team to dive into day-to-day analyst work, build stakeholder relationships, and develop business fluence
• Build foundational data models, semantic models, documentation, and context that power conversational analytics across a variety of data domains (growth, product, business metrics, operations etc.)
• Approach opaque, unstructured problems by developing core analytic frameworks and standardized semantic models that shape the way the company thinks about our business
• Communicate and collaborate with different stakeholders to identify opportunities and build solutions that standardize our data approach to common problems across the company
• Teach and guide stakeholders on best practices for using LLMs for their own data needs, and distill what works into reusable training materials
• Develop and maintain AI evaluations for conversational analytics, tracking accuracy programmatically and building stakeholder feedback loops

What You'll Bring to the Team

• 4+ years of full-time working experience in quantitative analysis roles, with a portion of that time focused on data modeling in dbt
• Hands-on experience using LLMs for coding and analytics, going beyond asking simple questions and building data products through LLM workflows
• Expert-level SQL skills
• Experience using BI tools (ours are Hex and Looker) to effectively convey information
• Strong communication and teaching skills
• Strong problem solving skills, analytical aptitude, and numerical dexterity
• Demonstrated track record of project ownership

Nice-to-haves

• Experience with AI evaluation of LLM tools
• Experience developing data pipelines and processes
• Open to data analysts with demonstrable interest and project experience who are looking to make the transition to analytics engineer