We believe the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and infrastructure developers need to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo and SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 11,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Salt Lake City, Washington D.C., London and Amsterdam.
Making data-driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide tooling and guidance to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively. In addition, Plaid will not be successful if we can't move quickly. We build the data and machine learning infrastructure to enable Plaid engineers to prototype and iterate on products and features built on top of consumer-permissioned financial data.
Engineers on the Data Platform teamwork on the three workstreams within the team (analytics infra, production data infra, ML platform) to scale our existing data pipelines and build the various pieces of ML platform from the ground up.
We work in Python, Golang, and Typescript. Our systems are built on top of Docker, Kubernetes, Sagemaker, Spark, S3, Redshift, Airflow, and ElasticSearch.
Our engineering culture is IC-driven -- we favor bottom-up ideation and empowerment of our incredibly talented team. We are looking for engineers who are motivated by creating impact for our consumers and customers, growing together as a team, shipping the MVP, and leaving things better than we found them.