Parse.ly is a real-time content measurement layer for the entire web. Our analytics platform helps digital storytellers at some of the web’s best sites, such as Arstechnica, The New Yorker, TechCrunch, Wired, The Intercept, Slate, and many more. In total, our analytics system handles over 65 billion monthly events from over 1 billion monthly unique visitors.
Our entire stack is in Python and JavaScript, and our team has innovated in areas related to real-time analytics, building some of the best open source tools for working with modern stream processing technologies.
On the open source front, we maintain streamparse, the most widely used Python binding for the Apache Storm streaming data system. We have also contributed to the development of high-performance Kafka client libraries for Python.
Our colleagues are dedicated: our UX/design team has built one of the best-looking dashboards on the planet, using Vue.js and D3.js, and our infrastructure engineers have built a scalable, devops-friendly cloud environment, using tools like Ansible and Terraform.
As an experienced Backend Software Engineer, you will help us expand our reach into the area of petabyte-scale data analysis — while ensuring consistent uptime, provable reliability, and top-rated performance of our backend streaming data systems.
Parse.ly’s backend engineering team already makes use of modern technologies like Python 3, Storm, Spark, Kafka, and Elasticsearch to analyze large datasets. As a Backend Software Engineer at Parse.ly, you will be expected to master these technologies, while also being able to write code against them in Python, and debug issues down to the native C code and native JVM code layers, as necessary.
This team is responsible for a real-time analytics infrastructure that processes over 2 million pageviews per minute from over 5,000 high-traffic sites. It operates a fleet of cloud servers that include thousands of cores of live data processing. We have written publicly about mage, our time series analytics engine. This will give you an idea about the kinds of systems we work on.
