About OWL
Old Well Labs is a Charlotte-based financial intelligence startup used by leading allocators and the fund managers they invest with. Our platform turns billions of disclosures into structured data that makes it easy for those in the investment ecosystem to find, monitor, and connect with one another.
About the Role
We’re hiring an Engineer to define and build systems that make our data usable across the company.
This role will own the architecture for how data is processed, enriched, and operationalized. You'll build agent systems that collect data across the web and extract structured information, the queues and pipelines that route that data through human-in-the-loop review, and the admin tooling that lets our data ops team work efficiently. You'll work heavily on the data infrastructure underneath (e.g., SQS, dbt, Step Functions, Lambda) to make it all fast and reliable.
We're big proponents of using AI to do our best work, and this role is the most direct expression of that. Every workflow you build helps remove manual work or makes us faster at it. If you have an idea for a new tool, agent, or pipeline, you will be able to own and push that idea forward.
What You’ll Do
Build AI Agents That Collect and Extract Data
Design and ship agent workflows that collect data from across the web, extract structured information, and build it into our app.
Design Data Pipelines and Event-Driven Systems
Own how data flows from collection through processing to the platform. Work across SQS, dbt, Step Functions, Lambda, Athena, and S3 to evolve the architecture as volume and complexity grow.
Build Admin Tooling for Data Operations
Ship the human-in-the-loop interfaces, dashboards, and internal tools our operations team uses every day.
Move Quickly in a Small, Senior Team
Operate with high ownership in a small engineering team. Take problems from ambiguous requirements through design, implementation, and production, with the autonomy to make the calls that ship the work.
What We’re Looking For
- 5+ years of backend, platform, or systems engineering experience
- Strong Python and system design expertise
- Experience with distributed and event-driven systems
- Hands-on experience with data pipeline tools (dbt, Airflow, or comparable)
- Experience defining architecture across multiple systems
