The Standalone Metering team is building a streaming system to ingest, process, and store our customers' usage across all Datadog products, providing vital insight to a broad variety of users. This new system will enable both internal and external customers to leverage data in new ways. The technology we’re creating will unlock our next level of scale, positioning our teams to support the next 3-5 years of product expansion and revenue growth.
The Revenue Data Engineering Teams create the data processing pipelines that measure our customers’ usage across all Datadog products, providing vital insights to a broad variety of users. This group of teams is at the leading edge of any new product we release.
Build a distributed, high-volume streaming architecture
Use various open-source and in-house technologies
Work across the stack, moving fluidly between programming languages: Scala, Python and more
Join a tightly knit team solving hard problems the right way
Own meaningful parts of our processing infrastructure, have an impact, grow with the company
An engineer with a BS/MS/PhD in a scientific field or have equivalent experience
Experienced in building and operating data pipelines for real customers in production systems
Fluent in several programming languages (JVM & otherwise)
Skilled in wrangling huge amounts of data and enjoy exploring new data sets
Someone who values code simplicity and performance
Interested in working in a fast-paced, high growth startup environment that respects its engineers and customers
You are deeply familiar with at least one streaming framework such as Flink, Dataflow, Beam, or Storm
In addition to data pipelines, you’re also familiar with Kubernetes and cloud technology
You’ve built your own data pipelines or data intensive applications from scratch, know what goes wrong, and have ideas for how to fix it
We're on a mission to build the best platform in the world for engineers to understand and scale their systems, applications, and teams. We operate at high scale—trillions of data points per day—allowing for seamless collaboration and problem-solving among Dev, Ops and Security teams globally for tens of thousands of companies. Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way.