Описание вакансии
Условия работы
Whisk.com is a fast growing discovery platform that helps people decide what to eat. Tracks people’s food preferences for food personalisation, allows users to save recipes into a personal cookbook and create shopping lists that connect into online grocery stores. Our application is seen by over 40m people every month and we work with the largest food manufacturers, retailers and publishers in the US, UK and Australia.
Whisk accolades include a ranking in UK’s Top 50 Startups 2015 and winning multiple industry awards. Dan Cobley, MD Google UK/Ireland said “Whisk is making the moment to buy really matter”.
We’re looking for people who get as excited about tech and our products as we do! You'll be expected to work closely with the everyone in the business, including the founders of the ventures.
The role
Although at the moment Scala is the primary programming language for Whisk backend, there are couple of new modules using Kotlin which were introduced recently. The system works as a set of microservices talking to each other through gRPC (Protobuf). Services are all running on top of Kubernetes. We use number of database technologies tailored to services specific needs: MongoDB, Mysql, Elasticsearch, Neo4j, Redis.
Architecture include real-time data processing layer built on top of Google Cloud Dataflow and PubSub.
We are looking for senior engineer to become part of backend team and continue driving Whisk technology forward.
Your qualification
- Strong Scala/Kotlin language and ecosystem knowledge
- 3+ years of software development experience
- Solid experience in working with distributed systems and databases
- Designing API
- Experience with cloud platforms, devops
- Knowledge of docker ecosystem
Beneficial
- Building distributed environments with Kubernetes
- Performance tuning and monitoring of JVM applications
- Experience running and monitoring/tracing gRPC services
- Familiarity with Mongodb, Mysql, ElasticSearch, Neo4j
- Experience with Google Cloud Platform
- Experience with stream processing engines (Storm, Spark, Google Cloud Dataflow, Kafka Streams)
- Experience with Machine Learning and NLP
- Viktor Taranenko ( https://uk.linkedin.com/in/viktortnk )