Data Engineer

2 weeks ago

Athens, Attica, Greece Kaizen Gaming Full time €60,000 - €80,000 per year

Let's start with the role

At Kaizen, we aim to harness the power of modern AI algorithms to continuously improve our product and business. Kaizen's AI department consists of product, platform and research teams and lies at the core of this mission. We develop AI products for various applications, ranging from detecting fraudulent activity to providing personalized gaming recommendations, and from building AI-powered co-pilots to optimizing CRM strategies. 

Our teams are designed our teams to be self-driven, autonomous and cross functional. Within these teams, you will have the opportunity to work with Data Scientists, Machine Learning Engineers, Data Engineers, Product Analysts, Product Owners and Agile Delivery Leads.

As a Data Engineer, you will focus on delivering robust, and scalable data pipelines that transform raw data into features that can be used in Machine Learning models. 

As a Data Engineer your will:

  • Design, develop and maintain scalable data processing pipelines in a distributed, and data-intensive production environment;
  • Design, develop and maintain microservices in Python that serve these features on production;
  • Design, develop and maintain internal tools that enable CI/CD pipelines, experiment tracking and data versioning;
  • Ensure data integrity by employing data quality processes.

What you'll bring:

  • Proficiency in software development with Python in large scale, high-performance production environments;
  • Hands-on experience with Spark and PySpark;
  • Deep knowledge with data modeling and expertise in handling both structured and unstructured data;
  • Hands-on experience with Apache Kafka;
  • Exposure to cloud environments;
  • Familiarity with Distributed Software System design;
  • Excellent problem- solving and communication skills.

It's a plus if you bring

  • Hands-on experience with the Databricks Platform;
  • Experience with streaming data and real-time data processing frameworks;
  • Experience with NoSQL DBs, such as Redis and Neo4j;
  • Good understanding of ML algorithms and the ML-project lifecycle.

#LI-Hybrid 

#LI-EL1

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