DeepSource continuously analyzes source code changes and finds issues categorized under security, performance, anti-patterns and bug-risks. DeepSource integrates with GitHub/GitLab and runs analysis on every commit and pull request, discovers and fixes potential issues before they make it to production.
There are over 40 million developers in the world, and all of them write and review code in some form. DeepSource is working on building tools that help developers ship good code. We believe there’s a massive opportunity to impact how software is built right from where the code is written using automation and intelligence, which not only improves developer productivity but also increases software’s robustness.
DeepSource is seeking an energetic and diligent Customer Success Manager to join the team. This champion of the DeepSource mission will help build and maintain relationships with our customers to ensure their happiness and prosperity. You will be responsible for being proactive to get to know DeepSource customers' broader challenges and goals so that you can identify all the most creative and effective ways they might leverage DeepSource. If you're successful, our clients will trust and respect your advice & expertise, will understand and appreciate the value-add DeepSource brings to them, and thus will be more likely to maintain – and grow – their relationship with us. You will work cross-functionally with Marketing, Sales, and Engineering to create optimal experiences and bridge the gap between product and customers. This is a highly cross-functional role — you'll collaborate with groups across the organization to ensure the success of your accounts. You'll know our product better than anyone and help drive product improvements and company growth.
Prior experience with customer onboarding, renewals and expansions.
The role is remote within North America. You're welcome to work from our beautiful San Francisco office.
Our front-end web servers are written in Python (Django) and analysis orchestration systems are written in Go. We use Kubernetes for scheduling and analysis orchestration. We have the following teams: Platform, Analyzer, Infrastructure. Our analyzer team works primarily on adding new issues, optimizing existing issues to reduce false positives on all of our analyzers.