data engineer in iGaming

Greencastle Digital operates in the technology, information and media, and gambling facilities and casinos industries. The company uses data platforms to support business intelligence, analytics, customer acquisition, operational performance, and sports betting activities.

• Design, develop, and maintain scalable batch and real-time data pipelines;
• Build ELT workflows for large datasets from internal and external sources;
• Develop and maintain cloud-based data infrastructure using Amazon S3, AWS Glue, AWS Lambda, and Amazon Redshift;
• Implement orchestration and scheduling workflows using Apache Airflow or Amazon MWAA;
• Develop data transformations and modelling processes using SQL and dbt;
• Integrate streaming and large-scale processing solutions using Apache Kafka and Apache Spark;
• Ensure the reliability, performance, and scalability of production data pipelines and infrastructure;
• Participate in scheduled on-call rotations to monitor, troubleshoot, and resolve high-priority data pipeline issues during weekends and bank holidays;
• Support data integration into enterprise data warehouses such as Snowflake or Redshift;
• Implement secure data handling practices within cloud-based data platforms;
• Support the management of data access, permissions, and role-based controls across data systems;
• Ensure data pipelines and storage systems comply with internal governance policies and regulatory requirements;
• Maintain high standards of data quality, integrity, and traceability;
• Contribute to data governance frameworks and best practices;
• Collaborate with data architects to design and evolve scalable data architecture;
• Translate business requirements into technical data solutions with product teams, analysts, and business stakeholders;
• Partner with IT and infrastructure teams to support system integration and platform stability;
• Participate in agile development practices, including sprint planning, reviews, and technical discussions;
• Communicate technical concepts to technical and non-technical stakeholders;
• Identify and implement opportunities to automate manual data processes;
• Improve pipeline reliability, monitoring, and operational efficiency;
• Redesign and optimize data infrastructure for increasing data volumes and evolving business requirements;
• Evaluate and introduce new tools, technologies, and best practices to enhance the data platform;
• Promote innovation and continuous improvement within the data engineering team.

• At least 3 years of professional experience in data engineering or a related technical role;
• Hands-on experience with cloud data platforms, particularly Amazon Web Services;
• Strong experience with large-scale production databases and SQL-based data warehouses such as Amazon Redshift, Snowflake, or Google BigQuery;
• Proven experience developing ELT pipelines using dbt, Apache Airflow, and Python;
• Familiarity with Apache NiFi, Apache Kafka, and Apache Spark;
• Experience with Docker and Kubernetes;
• Strong SQL and data modelling capabilities;
• Proficiency in Python for data pipeline development;
• Strong analytical and problem-solving skills;
• Ability to work with complex and large-scale datasets in production environments;
• A proactive and innovative mindset focused on continuous improvement;
• Ability to adapt quickly in a fast-paced and evolving technical environment;
• Strong communication and collaboration skills;
• Excellent English communication skills with the ability to convey complex technical concepts to international teams;
• Willingness to contribute ideas, share knowledge, and participate in technical discussions.

• Participation in a scheduled on-call rotation during weekends and bank holidays.