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A consultant BI Data Engineer builds and maintains data platforms for a European institution: ETL/ELT pipelines, data warehouse/lakehouse architectures, and Power BI reporting. Core stack is Microsoft Azure (Synapse, Data Factory, ADLS Gen2, Fabric), Python, SQL, and Power BI, with Git/Azure DevOps in an agile setup.
Ayesa Digital is hiring a BI Data Engineer to join its international data team supporting a European institution, designing and maintaining data platforms, EDW, Data Lake, and BI solutions. Day-to-day work centers on Azure services, Python, SQL, and Power BI to build scalable pipelines, dashboards, and data models.
Ayesa Digital in Sevilla is hiring a Data Scientist / ML Engineer to build and evaluate machine learning and deep learning models end to end — data preparation, feature selection, training and evaluation — on AI and predictive-models projects. Core stack: Python, NumPy, pandas, SQL, scikit-learn, and PyTorch or TensorFlow.
Develops and maintains Python applications and participates in AI/Machine Learning projects for high-level banking-sector clients, designing data-driven models and integrating APIs. Requires 2+ years of professional Python; hybrid role based in Bizkaia, Spain (4 days remote, 1 day onsite).
Senior backend developer developing and optimizing server-side business logic, backend components, and integration web services at a financial-sector client's offices in San Isidro, Lima. Core stack: .NET Core/C#, Java (Spring Boot), Node.js, or Python, with SQL Server/Oracle databases and Git-based version control.
Ayesa Digital está buscando un/a Desarrollador/a ETL con experiencia real en Informatica PowerCenter para unirse a su equipo en España. Participarás en el desarrollo y mantenimiento de cargas ETL, mappings y workflows…
Cada profesional de nuestra empresa es importante para nosotros, nos ayudan a crecer de manera diversa y gracias a ellos somos más de 11.000 personas trabajando con el mismo objetivo en 10 países. Si eres una persona…
Data Scientist/ML Engineer responsible for the full data lifecycle—designing, training, and deploying machine learning models in production environments using Python, SQL, and ML frameworks.