Data Engineer

In this role you design, build and maintain scalable data pipelines and platforms to power analytics and marketing effectiveness across Entain’s digital brands. You will work within the Group Analytics Data Engineering Team to deliver robust data solutions that drive growth and improve decision-making. The role combines strong technical craftsmanship with real business impact in a fast-paced environment. You will partner with analysts, data scientists and marketing stakeholders to shape the future of data-driven decision-making at Entain.

• regular bonus
• pension
• 25 days annual leave
• wellbeing and development days
• Life assurance
• Private healthcare

• Design, build and maintain scalable data pipelines transforming raw data into business-ready products
• Enhance data quality frameworks, monitoring and validation processes
• Translate business and marketing requirements into effective data solutions and challenge assumptions
• Contribute to solution architecture and manage delivery from discovery to implementation and support
• Collaborate with central data, platform and engineering teams to align with enterprise standards
• Improve processes, datasets and tools to increase efficiency and business value
• Communicate technical concepts to technical and non-technical stakeholders, including senior leaders
• Champion best practices in testing, observability, documentation and maintainability
• Evaluate new data sources and technologies to expand analytical capabilities
• Define requirements and deliver pragmatic solutions in a fast-changing environment

• Strong experience building and maintaining data pipelines using SQL, Python, or similar technologies
• Experience with modern cloud data platforms (Snowflake, Databricks, Redshift, Synapse, BigQuery, or Azure Data Lake)
• Cloud-based solution development experience (AWS preferred; Azure or GCP acceptable)
• Familiarity with ELT frameworks, ideally dbt
• Experience implementing data quality, testing, monitoring, and observability tools (e.g., Monte Carlo)
• Experience with workflow orchestration tools (Prefect preferred; Airflow, Dagster, or Azure Data Factory as alternatives)
• Understanding of event-driven architectures and large-scale data platforms
• Source control and CI/CD practices (GitLab, GitHub, or Azure DevOps)
• Familiarity with containerisation (Docker or Podman) and IaC tools (Terraform)
• Strong data modelling, warehousing and analytics engineering practices
• Excellent stakeholder management and communication skills

• strong communication
• stakeholder management
• collaboration
• SQL
• Python
• Snowflake