paid / remote / full-time
Meet Railz.
Railz is a FinTech company that provides a single API to all major accounting software (QuickBooks, Xero, Sage, etc.) service providers and enables on-demand access to financial transactions, analytics, insights and reports on small business customers.
Our solution suits any lender, financial institution, accounting firm, auditor and tech developer that requires financial data on its small business customers for the purpose of assessing the financial health of a business.
Our Data-as-a-Service solution allows our customers to be up and running in hours. We provide quick, low cost and direct access to both existing and new customers’ accounting software via our single API.
Who You Are:
You are currently an experienced R user that appreciates reproducible clean code. You might already be talented at data engineering or have other general extensible computer science skills from a background in data analytics or data science, but you also enjoy optimizing data systems and/or building them from the ground up.You are mostly self-directed and comfortable supporting the data needs of multiple teams, systems and products and you are excited by the prospect of optimizing or even re-designing our company’s data architecture to support our next generation of products and data initiatives.
In this role, you will be responsible for expanding and optimizing our data and data pipeline architecture in R, developing ETLs that streamline the processing of financial data. You will also support our team with data initiatives and you will ensure optimal data delivery architecture is consistent throughout our Accounting-Data-as-a-Service product.
The Team:
The Data Engineering team works closely with the Data Science team & other backend teams on the API Squad, where we collaborate to create new integrations with Accounting Software Providers. The team & squad is distributed across the globe in an asynchronous environment. We move and build quickly with a good work-life balance always in check.
Responsibilities
- Code and build awesome tech in R.
- Create and maintain optimal data pipeline architecture.
- Assemble large, complex data sets that meet functional / non-functional business requirements.
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using R and AWS 'big data' technologies.
- Create data tools for analytics and different team members that assist them in building and optimizing our product into an innovative industry leader.
Skills and Qualifications
- Advanced working R knowledge and experience working with data engineering related packages (e.g. purrr, tidyr, dplyr, tibble, & the tidyverse at large).
- Experience with R package development and building production-quality software using R (e.g. plumber APIs, Shiny apps)
- Strong analytic skills related to working with unstructured datasets and bringing it into R (e.g. jsonlite, readr, xml2).
- Experience with relational data stores, SQL and NoSQL databases, including MongoDB (e.g. aws.s3, mongolite)
- Experience with RStudio and debugging code when things go awry.
- Experience with containerization and Docker.
- Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement (e.g. profvis, microbenchmark).
- Working knowledge of message queuing, stream processing, and highly scalable 'big data' processes (e.g. Spark, Kafka, Rabbit).
- Strong project management and organizational skills (we use Atlassian [JIRA, Bitbucket] and follow an agile/kanban approach)
- Bachelor's Degree, Master's Degree, and/or PhD in a relevant discipline and/or equivalent experience.
Bonus Points:
- Prior experience with SaaS products and startups
- Experience working with payment and financial platforms (e.g. Stripe, QuickBooks, etc.)