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, The Wall Street Journal, TechCrunch, The Intercept, Mashable, 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 also maintain pykafka, the most performant and Pythonic binding for Apache Kafka.
Our colleagues are talented: our UX/design team has also built one of the best-looking dashboards on the planet, using AngularJS and D3.js, and our infrastructure engineers have built a scalable devops-friendly cloud environment.
As a Python Data 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.
We’re the kind of team that does “whatever it takes” to get a project done.
Parse.ly’s data engineering team already makes use of modern technologies like Python, Storm, Spark, Kafka, and Elasticsearch to analyze large datasets. As a Python Data 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 owns a real-time analytics infrastructure that processes over 2 million pageviews per minute from over 2,000 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.
For this role, you should already be a proficient Python programmer who wants to work with data at scale.
In the role, you’ll...
Our distributed team is best-in-class and we happily skip commutes by working out of our ergonomic home offices. Here's a photograph of our CTO's setup running two full-screen Parse.ly dashboards.
Parse.ly is a fully distributed team, with engineers working from across the world. People with past experience working remotely will be prioritized. US/Eastern timezones will be prioritized.
Send a cover letter, CV/resume, and optionally links to projects or code, to the email address here: work@parsely.com. Make sure to indicate you are applying for the "Python Data Engineer" role.