Data Engineer

• Design and implement robust, scalable data architecture to support the collection, storage, and processing of large volumes of igaming and customer data
• Build and maintain efficient ETL and ELT processes to ensure seamless data ingestion from multiple sources into our data lake, DWH, and final consumers
• Create and maintain well-structured data models that ensure accuracy, integrity, and optimal performance for analytical use cases following Kimball best practices
• Collaborate with cross-functional teams to integrate data across systems, with several types of integration as API, Kafka, DB extractions, Webscrapping, Email, etc.
• Develop and manage real-time data pipelines using streaming technologies (e.g., Kafka) to support time-sensitive use cases
• Enforce data governance policies and best practices, ensuring high standards for data quality, security, and regulatory compliance
• Continuously monitor, troubleshoot, and optimize data pipelines, queries, and infrastructure for speed, efficiency, and reliability
• Support and expand production-grade orchestration workflows using Apache Airflow
• Set up and maintain monitoring, alerting, and observability for data pipelines and systems using tools like Elementary, Datadog, Prometheus, or Grafana
• Develop and maintain CI/CD pipelines for data projects using Bitbucket Pipelines or GitHub Actions, ensuring streamlined deployment and testing processes
• Follow best practices in Git-based version control and work collaboratively in a modern DevOps environment.

Senior Data Engineer

• Design and implement ingestion pipelines to onboard data from the core gaming platform, handling event-driven, transactional, and behavioural data at scale
• Build and optimise data transformation and orchestration workflows that prepare ingested data for downstream consumption
• Work closely with the Head of Data, Data Architect, and wider engineering team to deliver the integration layer on an accelerated timeline
• Ensure pipelines are reliable, observable, and designed for both real-time streaming and batch processing workloads
• Act as the team’s point of reference for data science work as your area of specialism, growing this focus over time as the platform matures
• Design and build predictive models and analytical products across key business domains including player segmentation, fraud detection, risk modelling, and personalisation
• Collaborate with key stakeholders to identify high-value opportunities for data science and translate business problems into analytical solutions
• Contribute to the definition of engineering standards, tooling, and best practices across the data ecosystem
• Build systems that are scalable, maintainable, and resilient
• Support large-scale platform and data migrations, including onboarding Tier 1 brands or high-volume partners onto new systems
• Contribute to migration frameworks, automated tooling, and data validation mechanisms to ensure data integrity and minimal operational disruption
• Work within a development environment that embeds agentic AI tools within the engineering lifecycle
• Leverage AI-assisted workflows to improve code development, pipeline reliability, testing, and operational efficiency
• Promote an automation-first mindset, embedding automation into data engineering and data science processes wherever possible
• Implement data validation, monitoring, and observability mechanisms across ingestion and transformation layers
• Ensure high levels of data integrity, reliability, and availability
• Work closely with engineering, analytics, and product teams to deliver reliable and well-structured datasets.

Big Data Engineer

• Design and develop data pipelines using Python (PySpark), applying best practices for performance and reliability.
• Ingest and integrate (real-time, batch) diverse data sources into the data lake, ensuring data quality and accuracy.
• Maintain data models and the overall data estate codebase.
• Daily monitoring and operations on cloud data platforms (primarily Azure/Microsoft Fabric).
• Monitor data processes, pipelines, troubleshoot issues, and support daily data operations.
• Collaborate with engineers, analysts, and business stakeholders; support ad hoc data analysis requests.
• Drive continuous improvement through training and self-development.
• Any other ad hoc tasks that may be required by the business from time to time.

• Design, build, and maintain robust, scalable, and reliable streaming and batch data pipelines in our modern cloud environment
• Map and profile data lineage from source systems to target environments
• Implement cutting-edge data engineering best practices to guarantee the quality and consistency of our crucial data assets
• Troubleshoot and resolve complex issues with data feeds, ensuring uninterrupted flow and availability
• Establish and maintain strong communication channels between business and technical teams regarding the strategic use of company data
• Full ownership of the development lifecycle and ability to work independently if necessary

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