Position: Hadoop Security Engineer
Company: Tensill
Experience required: 5y
English level: upper

Our client is the world's largest travel site, and we operate at scale with over 750 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 Platform team is responsible for maintaining our petabytes-scale data lake and the ecosystem of tools that surround it. We are looking for talented engineers to help us take it to the next level!

The ideal candidate will have hands-on operational experience with Big Data technology and in securing distributed platforms. Our stack is a hybrid open-source Hadoop cloud, house-built distributed ETL tools, and public cloud
technologies that help to drive the analytics of the company. A successful engineer will be able to solve complex security problems in our hybrid-cloud environment while working on a team spanning multiple time-zones. 

**What you will be doing:**
- Writing code! This is an engineering role, after all
- Using your expertise to assist in the design and implementation of a secure modern Big Data, Hybrid Cloud Ecosystem
- Working with a team of talented engineers to identify the shortcomings in Big Data technologies
- Leading cloud engineering best practices and developing cloud-first solutions to Big Data challenges
- Moving quickly to create today's solutions to tomorrow's problems

**What you will bring to the team:**
- 5+ years of in-depth technical experience with modern Big Data technologies such as Hadoop, Hive, Presto, and Spark
- Experience with Kerberos and related Hadoop Security Technologies
- Experience with Apache Sentry or similar Role-Based Authorization tools for Hadoop
- Familiarity with the Linux operating system and the Bash shell
- General software engineering expertise; Java is preferred, but we also write a lot of Python and Kotlin
- The ability to communicate effectively in an operations environment
- Willingness to "get your hands dirty" in a modern Big Data environment