Position: Java Cloud/Big Data Engineer
Company: Rinf Tech
Experience required: 2y
English level: upper

We are a software development company with our own product, but we also deliver projects for top companies.

Work Scope:
Our new Engineer will be working with the design and implementation of data structures, data pipelines and stream processing using tools like Kubernetes, Cloud Run, Pub/Sub, Dataflow, Cloud Storage, Cloud SQL, Cloud Memorystore, BigQuery, Vertex AI.

The main programming languages are Java and Kotlin (also JavaScript, Python), and our focus is to build data storages for Analytics and BI services. Architecture and design will be based on agile Data Warehouse principles and best practices, such as using Data Lake / Data Mesh, Kimble, Data Vault 2.0 etc.

Strong understanding and hopefully experience within some of these methodology and principles is important.
Process and data flow (pipelines) implementation will preferably be developed using tools and technologies within Google’s stack and platform (GCP). This will be prioritized unless there are strong arguments for doing something else, such as implementations that cannot be delivered based on existing technologies delivered by Google.

For persons not having experience with GCP, a strong desire to learn new stuff and self driven motivation is critical in order to succeed.

What you will be working on:
• Contribute to our team’s improvement through honest feedback, but also by leading through example and knowledge sharing
• Manage implementations of Analytics and ML architecture
• Write readable, testable, and fault-tolerant code
• Review code designs and evaluate new technology
• Guide team members and other teams on how our tools and products work
• Ensure solution and operations reliability

What you offer us:
• At least 2 years of experience with Java, Kotlin, Spring Boot, RESTful services, Microservices Architecture
• Testing frameworks (JUnit 5, Hamcrest, Mockito)
• Clean Code, SOLID, GoF design patterns
• The basics and some working experience with SQL DBs; understanding
transactions, ACID, Isolation levels
• The basics of containerization, Docker, K8s, Linux, CI/CD pipelines, Maven, Git
• Understanding of distributed systems and Big Data concepts, clusters, replication, fault-tolerance, data locality, batch vs streaming, ETL vs ELT, DWH vs Data Lake, cloud, cloud-native, serverless, regions & availability zones, the CAP Theorem, ... (the more the better)

Big Plus to have:
• Good understanding and experience implementing multi-tenant SaaS solutions using Microservice Architecture and API-first design
• Quarkus, Kotlin, Apache Beam
• In-depth knowledge and working experience with Google Platform
• Any GCP certifications, especially Professional Data Engineer, Cloud Developer, or Cloud DevOps Engineer

Nice to have:
• Some theoretical knowledge and working experience with the Hadoop ecosystem, distributed processing engines, NoSQL databases (HBase, MongoDB, Cassandra), and query tools (Hive, Impala)
• GitHub Actions, Terraform
• Pact for contract testing, Gatling for load testing, SRE concepts, DORA metrics, Security concepts (JWT, OPA, Stryra)
• Basic knowledge of JavaScript, Python.