Senior Data Engineer

Founded in 2008, ComeOn Group is a leading global player in the rapidly growing iGaming market. Having launched its first online brand already in 2010, ComeOn Group runs a wide range of brands across several regulated markets, operating on its proprietary Casino and Sports Betting platform. ComeOn Group is an international employer with over 550 employees across 7 main locations.

• Minimum 5 years experience in data integration and real-time streaming.
• Programming experience with Python, SQL, and potentially Java.
• Understanding of data warehousing concepts, schema design (star schema, snowflake schema, denormalization), and optimization.
• Experience with distributed frameworks like Spark or Flink.
• Experience with GCP (preferred) or AWS cloud data processing solutions.
• Familiarity with BigQuery, Pub/Sub, Dataflow, and Datastream is a plus.
• Excellent spoken English.
• Strong attention to detail and data quality.
• Ability to work in a fast-paced environment interacting with various disciplines.
• Proven prioritization, problem-solving, and communication skills for issue resolution.

Nice to have:

• Academic degree in quantitative discipline such as computer science or mathematics.
• Experience in the iGaming industry and involvement in machine learning projects like personalised Sportsbook and Casino recommendations.
• Previous experience as a machine learning engineer.
• Experience with Airflow.
• Experience in business analysis and requirements gathering.

• Collaborate with Data Scientists to design, build, and maintain robust, scalable, and reliable batch and streaming data pipelines for training and deploying data science and ML models.
• Implement data engineering best practices to ensure quality, consistency, and lineage of model training data and feature sets.
• Troubleshoot and resolve complex data feed issues to ensure uninterrupted availability.
• Maintain communication channels among Business Stakeholders, Data Scientists, and Engineering teams for strategic machine learning project development and data use.
• Assume full ownership of the data science data development lifecycle from raw data ingestion to production model data serving, working independently as needed.