The Role
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 will have two primary focuses
- 1) building data models/semantic models, documentation, context, and evaluations to strengthen our LLMs’ ability to consistently achieve the correct answers, and
- 2) training non-data stakeholders to more efficiently and effectively interact with our data stack.
You will start your time by diving deep into day-to-day analyst work and as you build a strong understanding for how data is used by end stakeholders and build business context. You’ll then transition to building out tooling that allows stakeholders to self-service via Conversational Analytics. You take pride in developing tools, processes, and working frameworks and teaching your colleagues how to use them to better leverage data to generate insights and operationalize data. You have a bias for action, strong communication skills and a continual learning mindset.
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
We are open to data analysts with demonstrable interest and project experience who are looking to make the transition to analytics engineer
