Domino has an ambitious vision to change data science. Backed by Sequoia Capital, Zetta Venture Partners, Bloomberg Beta, and In-Q-Tel, we’re accelerating research at the world’s most analytical organizations including Allstate, Instacart, Monsanto, and S&P Capital IQ. Our platform allows access to scalable compute, compounds knowledge through a unique version management system and delivers insights to market faster through a host of publishing tools.
Tech Support Engineers work with customers to make that vision a reality. You are the first line in our Customer Success team to help our users get the most from Domino. You’ll field incoming questions, help users configure Domino and help them to understand how to use the features of the product. You’ll monitor the performance of Domino systems, making sure that customers have the right infrastructure for the work they do. You’ll also troubleshoot technical issues and remediate customer problems.
Ultimately, your role is to help our customers improve their research by capitalizing on Domino’s functionality and the best practices it enables.
Give great help to customers who send in questions or report problems
Configure the Domino platform to enable users
Diagnose and resolve production problems
Surface issues to Product and Engineering teams to improve the product
Bachelor's STEM degree
Great customer service skills and a sense of urgency
Great debugging, problem solving, and analytical skills
Great organizational, prioritizing, and communication skills
Willingness to join an on-call rotation during business hours
Experience with the following is a plus:
Familiarity with the Linux command line and server administration
AWS, Azure, GCP
Docker and Kubernetes
Python and/or R
Ubuntu and/or CentOS
Hadoop and/or Spark
About Domino Data Lab
Domino was founded by three veterans of the finance industry, to help leading organizations develop better medicines, grow more productive crops, build better cars, or simply recommend the best song to play next. Our mission is to help data scientists across industries develop and deploy ideas faster with collaborative, reusable, reproducible analysis.