OverviewA continuación, encontrará un desglose completo de todo lo que se requiere de los posibles candidatos, así como la forma de presentar su candidatura. ¡Mucha suerte!As a Data Engineer at Entain, you design and build scalable data pipelines and platforms that power analytics, marketing effectiveness, and decision-making across our digital brands. You will join the Group Analytics Data Engineering Team, delivering robust data solutions that enable insights, optimised campaigns, and commercial growth. Your work directly supports budget allocation, performance improvements, and regulatory reporting at scale. You will operate in a fast-paced, cross-functional environment where data quality, observability, and collaboration matter to impact the business.Compensaciones / Beneficiosregular bonuspension25 days annual leavewellbeing and development dayslife assuranceIncome Protection cancellation? (unknown context, keep as listed)ResponsabilidadesDesign, build and maintain scalable data pipelines turning diverse data into business-ready productsEnhance data quality frameworks with monitoring and validation to ensure trusted dataPartner with business, marketing and tech teams to translate requirements into solutionsContribute to solution architecture and manage delivery from discovery to implementationCollaborate with central data, platform and engineering teams to align with enterprise standardsDrive continuous improvement across processes,



datasets and tools to increase valueConvey technical concepts clearly to both technical and non-technical stakeholdersChampion engineering best practices in testing, observability, documentation and maintainabilityEvaluate new data sources, technologies and approaches to expand analytics capabilitiesThrive in a fast-paced environment defining requirements and delivering pragmatic solutionsRequisitos principalesStrong experience building and maintaining data pipelines using SQL, Python, or similarExperience with modern cloud data platforms (Snowflake, or Databricks, Redshift, Synapse, BigQuery, Azure Data Lake)Experience developing cloud-based solutions (AWS preferred or Azure/GCP equivalent)Familiarity with data transformation and ELT frameworks (dbt)Experience implementing data quality, testing, monitoring, xhfqzwm observability tools (e.G., Monte Carlo)Experience with workflow orchestration tools (Prefect preferred; Airflow, Dagster,AzureData Factory as alternatives)Understanding of event-driven architectures, distributed data processing, and large-scale platformsExperience with source control and CI/CD (GitLab, GitHub, or Azure DevOps)Familiarity with containers (Docker or Podman) and IaC tools (Terraform)Strong data modelling, warehousing and analytics engineering practicesclear communication with both technical and non-technical stakeholderscollaboration across cross-functional teamsability to prioritise and adapt in a fast-paced environmentSQLPythonSnowflake

📌 Aws Data Engineer (Tomares)
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📍 Tomares