ABOUT TRUEBILL 🔮
Truebill is a YC-backed startup with offices in San Francisco and Washington, D.C. Our DC office is right on the Silver Spring metro! Hundreds of thousands of people use Truebill to manage their daily finances and take control of their money. We just recently announced our $45 million Series D round of funding and are now looking to scale our all star team!
With a mission to improve the financial health of everyday people, Truebill is transforming the way people manage their expenses and grow their net worth. Through helping people cancel unnecessary subscriptions, negotiating bills, and securing refunds, we save our members money while helping them regain control over their finances.
Truebill is at an inflection point in the construction of its data team. We've relied mostly on off the shelf tools (Fivetran, dbt, Looker, Mode) to build a data stack that got us to a million paying members. While we can continue to operate in this mode, we've seen increasing headwinds that come with scale. Bigger and more complex queries, multiple joins across projects, and generally the challenges of having over 100 daily consumers pushing our analytics stack for answers. We've reached the next stage in our growth that we need additional structure and good, foundational patterns in our analytics stack that will allow us to scale in data volume, builders, and consumers. We're looking for someone who has done this before and is excited about the challenge of evolving modern analytics for both scale and impact.
ABOUT THE ROLE 🤹
In this role, you will:
- Work with data scientists and business leaders to build data pipelines for our marketing and internal operations. You will be responsible for maintaining the proactive measurement and alerting of data quality in our analytics stack
- Proactively refine and improve our analytics architecture and tooling stack with the end consumer in mind
- Help us speed up our business intelligence stack through effective caching and understanding the business use cases of stakeholders
- Work with the analytics team to tune our dbt implementation and bring common patterning and best practices to our aggregate and reporting layers
- Add additional sources of data to our analytics stack from third party vendors
- Team with data engineering colleagues to solidify our production data organization and architecture
- Monitor and remove PII from the analytics layer and work with data engineering and tooling partners to build robust solutions to upstream PII handling
- Incorporate both streaming event sources and static loads into a unified view for analytics and enjoy the challenge of modeling and unifying data across multiple rates
ABOUT YOU 🦄
- You have spent the last few years working in the analytics stack of a high-growth company and are familiar with the growing set of analytics tooling solutions. You want to help evolve a company to the next level of maturity
- You see the value of accurate documentation as one of the keys to scaling and are a strong written communicator
- You have production experience with some portions of our analytics stack (BigQuery/GCP, Fivetran, dbt, Looker, Mode)
- Experience partnering with both analytics teams and business stakeholders to deliver analytics solutions for both internal and external consumers
- You have strong opinions on how to handle PII and operate from a consumer privacy and security first mindset
- Nice to have: You've worked with ML and Data Science teams around creating standardized feature stores and tooling to enable rapid iterations
Truebill
