Spade

Spade is unlocking the next generation of fintech development. Transaction data is core to financial technology and Spade is ensuring companies can uncover the value locked within their customers data without investing valuable developer time, or hiring ML experts and data scientists.

Spade is a universal transaction enrichment API purpose-built for Fintech. With a single API call we take raw transaction data and add normalized merchant identities, categories, granular geo data, and more.

We were founded in Vancouver Canada but expect to be HQ'd in New York within the year. We've just closed a $1M pre-seed round with some incredible investors and are actively looking to expand our team. We're looking for exceptional engineers interested in solving challenging problems with autonomy.

The core of our product is our Enrichment API. We accept low quality banking/credit card data and return it in an organized and usable form with as much enrichment as possible. That means extracting the merchant names, finding a logo or graphic, locating the shop's address, purchase categorization (e.g. cafe, gym, ATM), finding websites, phone numbers, ownership information, etc. etc. etc. We try to aggregate as many fruitful data sources as possible to provide a complete picture of a transaction. After cleaning and organizing the transactions there are countless more insights to be derived from the clean data, and we are developing more products to utilize that data.

We are searching for backend engineers to improve our enrichment engine, to integrate with new data sources, and develop robust infrastructure. We're looking for people who want to work on interesting projects involving the following.

• API Design
• Data Engineering
• Robust Infrastructure
• Self Refining Systems
• Data Science

Machine learning is at the root of our enrichment engine, the output of which is the basis for further enrichment. We're looking for ML engineers, particularly those who have experience in NLP, to develop novel models and provide a deeper understanding of a transaction.

Current Tech Stack:

• AWS Serverless
• Postgres
• Python & Django
• PySpark
• Proprietary NLP models