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—providing always-on alerting, metrics visualization, logs, and application tracing for tens of thousands of companies.
Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way. We need you to design and build machine learning-powered products that help our customers learn from their data and make better decisions in real-time.
We extract and manage data and events from our core products and live systems to make them centrally available for our Data Science team in both batch and real-time ways. We enable Data Scientists to productionize their models and expose their data assets to the rest of the company.
If you’re excited to work on a fast-moving data engineering team with the best open-source data tools at high scale, we want to meet you.
- Build distributed, real-time, high-volume data pipelines and work together with others to enable high-scale Data Science
- Do it with Spark, Luigi, Kafka and other open-source technologies
- Work all over the stack, moving fluidly between programming languages: Scala, Java, Python, Go, and more
- Join a tightly knit team solving hard problems the right way
- Own meaningful parts of our service, have an impact, grow with the company
- You have a BS/MS/PhD in a scientific field or equivalent experience
- You have built and operated data pipelines for real customers in production systems
- You are fluent in several programming languages (JVM & otherwise)
- You enjoy wrangling huge amounts of data and exploring new data sets
- You value code simplicity and performance
- You want to work in a fast, high growth startup environment that respects its engineers and customers
- You are preferably familiar with Spark and/or Hadoop and know how to put machine learning models in production
Is this you? Send your resume and link to your GitHub if available.