Business Intelligence Analyst

Who we are

Tomo is a digital mortgage company on a mission to eliminate the greed and complexity in lending, helping homebuyers save thousands of dollars. Our AI-driven customer experience makes the lending process faster, less frustrating, and more affordable. By passing these savings directly to homebuyers, we eliminate excessive fees and inflated rates that cost Americans billions each year. Backed by top-tier venture investors, including Ribbit, DST, and NFX, Tomo is poised to become one of the most valuable fintech startups of our generation.

The elevator pitch… 

We're looking for a Business Intelligence Engineer who:

• Thrives exercising both their technical and business skills to solve complex problems that someone without either skillset would be unable to solve
• Is curious about how our business operates and uses data to understand why things change and how to make the business better
• Chooses the best tool for the job, whether it's using an LLM to derive insights from unstructured data or using a dashboard to track weekly progress
• Accepts the ambiguity of startups and is eager to collaborate with others to determine what needs to be done to drive our company forward
• Thrives in an entrepreneurial and inventive culture which moves fast and will require the BIE to carefully prioritize their time

This role is based in our Seattle, WA Office Monday - Wednesday and remote optional Thursday & Friday.

Things you'll be doing…

• Use data to answer some of our most pressing questions and solve problems large and small  
• Build dashboards and automated reporting that our business teams use to drive the company (we use Sigma as our visualization tool)
• Utilize a modern data stack (DBT, Snowflake, Databricks) to make data available to stakeholders in a timely, repeatable manner
• Collaborate with Product, Engineering, and Business teams to ensure we're capturing high-quality data
• Communicate insights and technical concepts effectively to both business and technical stakeholders, translating complex findings into actionable recommendations

What you will bring…

• 3-5 years of experience working with data and performing quantitative analysis
• SQL expertise - you write window functions in your sleep
• Experience expanding and maintaining ETL systems - we use DBT
• Python skills - some experience with data analysis packages like pandas, matplotlib, and statsmodels
• Solid foundation in statistics - you can explain a linear regression

You may also have the extras…

• Product/web analytics tools  
• Marketing attribution, CRM data models
• Experimentation, machine learning, or large language models
• It's totally fine if you don't know these as long as you lean in to learn

The expected annual compensation for this position will be between $115,000—$145,000 USD