Optimove is a global marketing tech company, recognized as a Leader by Forrester and a Challenger by Gartner. We work with some of the world's most exciting brands, such as Sephora, Staples, and Entain, who love our thought-provoking combination of art and science. With a strong product, a proven business, and the DNA of a vibrant, fast-growing startup, we're on the cusp of our next growth spurt. It's the perfect time to join our team of ~450 thinkers and doers across NYC, LDN, TLV, and other locations, where 2 of every 3 managers were promoted from within. Growing your career with Optimove is basically guaranteed.
Responsibilities
We are a team of technophiles who love big things that scale. As the Automation Engineer responsible for all of our group’s services, you will need to:
We are a team of technophiles who love big things that scale. As the Automation Engineer responsible for all of our group’s services, you will need to:
- Create an automation that validates our systems meet the business requirements end to end.
- Develop and maintain solutions that read large amounts of data from sources like Kafka on the one hand and Snowflake on the other.
- Be part of the product ownership team - ensure that clients get the value we promise we deliver.
- Experiment by taking technologies to their edge, learn where things break, and develop profound understanding and insights.
- Work autonomously and collaboratively with the group’s team leads and other Optimove teams.
Requirements
- 5 years of experience in Automation / Backend / Data hands-on roles.
- Know at least 5 of the following technologies: Selenium / Appium, Kafka, NodeJS, DotNet, Python, Terraform / Cloud Formation, NodeJS, Snowflake, Redash / Superset / Metabase / Tableau, Spark / Hadoop, AirFlow, Apache Flink.
- Deep “under the hood” understanding of 1 of the technologies above.
- Data-driven mindset.
- Fluent in English.
Advantages
- 3+ years of Data Engineering QA Automation experience.
- 3+ years of Backend QA Automation experience.
- 3+ years of building Data Analysis Data Pipelines.