Pave is building a Cashflow API that allows fintech developers and data scientists to connect their data sources and retrieve clean & unified insights into their consumer's spending habits, bills, earnings profile, and account health.
We're abstracting the data cleaning and transformation efforts necessary to get a single view of a consumer’s finances so that fintech companies can rapidly build budgeting features, recommendations engines, risk models, and more.
We believe that by lowering the friction of extracting financial insights from the growing number of data sources, we can help accelerate the development of innovative and inclusive financial products. We’re on a mission to enable builders to reinvent and lower the cost of financial services.
There are a growing number of companies helping people gain access to digital financial services, whether it’s low-interest loans, payment installment solutions, integrated insurance offerings, fee-less banking, personalized budgeting, wage advances, or investment solutions.
We believe financial services will exist for every consumer segment, especially underserved segments including female professionals, refugee communities, creators, immigrants, millennials, retirees, gig workers, and so forth. We are not original in this thought - this is already happening.
Building equitable financial services for underserved consumer segments requires expensive resources, infrastructure, and analysis into new types of datasets. However, the cost to clean, transform, and analyze these new datasets is high, and every company is forced to do the same bespoke work on the same data sources. This is significant time and cost, and impeding the mission around expanding financial inclusion.
We are looking for a software engineer with a background in data infrastructure and is eager to build simply and rapidly to meet the growing needs of our customers.
We are currently building our platform from the ground up and as our fifth engineer, you will have direct influence over the architecture of the platform.
We are building on a modern stack consisting of Python, FastAPI, AWS Lambda, RDS Postgres, SageMaker, EKS, Apache Airflow, Pants Build and Github Actions. We want to leverage the industry best practices to deploy multiple times per day and go through fast iterations on our product.
Develop a system that utilizes both rules-based and machine learning methodologies necessary to clean and transform hundreds of millions of raw bank transactions into insights our customers can depend on for powering their use cases.
You will also help build and implement the data infrastructure that allows future Pave engineers and data scientists the ability to prototype and iterate on features to be executed on top of consumer-permissioned financial data.