TripAdvisor, the world’s largest travel site, operates at scale with over 760 million reviews, opinions, photos, and videos reaching over 490 million unique visitors each month. We are a data driven company, and we have lots and lots of data!
The Data Warehouse team at TripAdvisor is responsible for maintaining the petabyte-scale data lake, and we are looking for the right dev-ops engineer to help us take it to the next level.
The ideal candidate will have hands-on and operational experience with Big Data technology. Our technology stack is a hybrid of open-source Hadoop/CDH cloud and house-built distributed platforms to help drive the analytics of the company. We’re also increasing our footprint on AWS and designing new platforms that integrate our on-prem environments with those. A successful engineer will need to be able to solve problems in a complex and distributed environment, as well as communicate and interact with other teams and engineers.
What you will be doing:
- Writing code! This is an engineering role, after all.
- Working with a team of engineers to identify and improve upon the operational deficiencies of modern Big Data systems.
- Using your expertise to help scale a multi-datacenter analytics platform.
- Jumping head-first into complex code to implement solutions to operational shortcomings.
- Leading engineering best practices, especially those surrounding build systems and development environments.
- Moving quickly to create today’s solutions.
What you will bring to the team:
- 5+ years of Dev-Ops experience
- 5+ years of in-depth technical experience with Big Data technologies such as Hadoop, Hive, Presto, Kafka, or Spark
- Proven experience with Amazon Web Services technology, including S3, EMR, Glue, and Athena
- Familiarity with the Linux operating system and Bash shell
- Familiarity with build systems such as Jenkins
- General software engineering expertise; Most of our codebase is in Java, but we have a lot of Kotlin, Python, and Shell code as well.
- Ability to communicate effectively in an operations environment
- Willingness to "get your hands dirty" with Big Data