Stash is on a mission to simplify and democratize investing. We provide guidance and help investors learn the basics so they can do it themselves with as little as $5.
Stash is looking for a Data Engineer to join our growing team. You will build data solutions and distributed services that will help to productionize our data. You will work in a cross functional agile team and support the architectural design decisions and implementation of our data infrastructure as we scale. You will continuously experiment, iterate, and deliver on new product objectives that will make Stash a smarter product for its users. Overall, your work will help people traditionally underserved by the current financial industry learn how to invest, save, and better their financial positioning.
Our team currently uses Python, Scala, Akka, Pandas, Play, Spark, Heroku, MongoDB, and AWS. We always strive to choose the best tools for the job!
What you'll do:
- Design, develop, and deploy a scalable, distributed data pipeline
- Productionize our machine learning models and algorithms into data-driven feature MVPs that scale
- Drive data solutions and features that will impact business decisions and our product road map
- Leverage best practices in continuous integration and deployment to our cloud-based infrastructure
- Optimize data access and consumption for our business and product colleagues
- Develop an understanding of key product, user, and business questions
Who you are:
- 2+ years of professional experience working in a product-driven environment
- BS / MS in Computer Science, Engineering, Mathematics, or a related field
- You have experience building large-scale data products
- You have a deep understanding of system design, data structures, and algorithms
- Experience or interest working with distributed computing and using Spark, Hadoop, or MapReduce
- Experience working with (or a strong interest in) Python or Scala
- You are a self-driven, highly motivated individual who loves to learn new things!
Nice to haves:
- Experience in Machine Learning and Information Retrieval
- Experience building recommendation systems
- Experience with experimental design and research
**No Recruiters Please