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The Data Engineer
The Data Engineer is responsible for building and maintaining scalable, reliable, and high-quality data pipelines and infrastructure that power data products and analytics across the organisation.
The role focuses on ensuring data is accurate, timely, and consistently processed, particularly in environments involving high-volume transactional and API-driven systems.
Key Responsibilities
- Data Integration
- Data Transformation & Modelling Support
- Data Quality, Monitoring & Reliability
- Performance & Scalability
- Collaboration
Required Skills & Experience
- Data Pipeline Development
- Design, build, and maintain ETL/ELT pipelines
- Ingest data from internal systems and external APIs
- Develop scalable data processing workflows (batch and/or real-time)
- Ensure pipelines are reusable, efficient, and maintainable
- Integrate data from various sources, including transactional flows
- Handle complex data scenarios such as:
- Event sequencing (e.g. bet → resolve)
- Idempotency and duplicate handling
- Partial or delayed data
- Ensure consistency between source systems and analytical datasets
- Implement transformation logic aligned with architectural data models
- Build and maintain structured data layers for analytics consumption
- Collaborate closely with the Data Architect on model implementation
- Implement data validation, monitoring, and alerting mechanisms
- Identify and resolve data inconsistencies or failures
- Ensure high levels of data accuracy and availability
- Optimise pipelines for performance and cost-efficiency
- Support scaling of data infrastructure as volumes grow
- Ensure low-latency data availability where required
- Work closely with Data Architect, Analysts, and Manager
- Support Analysts by ensuring availability of curated datasets
- Contribute to continuous improvement of data platform capabilities
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Nice to Have
- 3–7+ years experience in data engineering, backend engineering, or similar roles
- Strong programming skills (e.g. Python, SQL)
- Solid understanding of ETL/ELT processes and data pipeline design
- Experience working with APIs and integrating distributed systems
- Experience handling transactional or event-based data
- Strong understanding of:
- Data transformation techniques
- Data warehousing concepts
- Data modelling fundamentals
- Experience with data orchestration and workflow tools
- Ability to build robust, fault-tolerant systems
- Strong problem-solving skills with attention to detail and data accuracy
- Experience in iGaming, fintech, or high-volume transactional environments
- Experience with event streaming technologies (e.g. Kafka)
- Familiarity with modern data stack tools (e.g. Snowflake, BigQuery, dbt, Airflow)
- Experience with real-time or near real-time data processing
- Understanding of idempotent processing and event ordering
- Exposure to CI/CD and infrastructure-as-code practices
- Experience supporting analytics or BI teams
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Success Metrics
- Reliability and uptime of data pipelines
- Data freshness and latency
- Reduction in data errors and inconsistencies
- Performance and scalability of data processing
- Availability of high-quality, curated datasets for analysts
What’s in it for you?
- Experience a dynamic and team-orientated work environment.
- Opportunities for personal growth and learning
- An open, inclusive and supportive team where you will be valued, and your suggestions will be welcome.
- 26 days paid holiday per year. This is in addition to local bank holidays.
- Competitive salary
- €400 annual wellness Allowance
- Hybrid Working
- Risk Benefits such as pension, Life Assurance (4x annual salary), Private Medical Insurance
- Team Building Opportunities
- Flexible core hours between 10am – 4pm
- Receive support whenever you need it with our Employee Assistance Program, available 24/7.
- Local discounts and more.
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Skills
Data Integration
Data Transformation
Data Modelling
Data Quality
Data Monitoring
Data Reliability
Performance Optimization
Scalability
Python
SQL
ETL
ELT
APIs
Data Warehousing
Data Orchestration
Event Streaming
Location