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The Lead Architect will oversee the technical direction for enterprise agentic AI solutions, focusing on cloud, data, and full-stack engineering. The role involves mentoring engineers, establishing deployment standards, and selectively performing hands-on work to ensure project success and team independence.
This is Us: JS Perkins Consulting (JSPC) delivers value added management and technology consulting services. JSPC is committed to creating trusted partnerships to provide sustainable solutions to meet our client needs.…
¡En Ayesa Digital crecemos Contigo! 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…
We’re looking for a Data Engineer – Migration to join our Data Foundation team and play a key role in designing robust data pipelines and delivering complex data migration initiatives. In this role, you’ll work at the…
En Keepler (part of Accenture) queremos hacer crecer nuestro equipo con personas que tengan ganas de desarrollar software basado en datos con dos objetivos: ayudar en la transformación a nuestros clientes y disfrutar…
Data Engineer Senior (Azure & Databricks) – Sector Seguros Ubicación: Un día presencial en oficina de Barcelona Buscamos perfiles de Data Engineers para unirse a un proyecto estratégico de larga duración en el…
Data Scientist developing ML forecasting models to balance Denmark's electricity grid, using Python, PySpark, Azure, and Databricks within Energinet's Digital Balancing Products team in Fredericia.
Leads data analytics and systems management by building pipelines, optimizing databases, and creating business intelligence solutions using C#, Azure Databricks, and Power BI to transform data into actionable insights for stakeholders.
Senior BI Developer designing, building, and optimizing data intelligence solutions using Microsoft (Power BI, Fabric) and IBM (Cognos, DB2) platforms, with heavy emphasis on T-SQL, ETL, data warehousing, and data modeling to support strategic decision-making.
Senior ML Engineer building self-service ML platform tooling—model packaging, deployment, CI/CD, and observability—using Python, PyTorch, TensorFlow, Docker, Kubernetes, MLflow, and GitHub Actions at a lottery and sports betting company in Toronto.
Leads AI/data engineering solutions on RBC’s hybrid multi-cloud platform, designing scalable pipelines, RAG systems, and agentic workflows to meet business objectives. Mentors teams and ensures robust, governed data products for financial AI applications.
Cloud Data Engineer (5+ years) to configure cloud environments and build data pipelines using Azure Synapse, ADF, Databricks, and Spark Our transportation client is seeking a Cloud Data Engineer (5+ years) to configure…
Descripción del puesto: Buscamos un/a Data Engineer especializado en PySpark y Python para incorporarse a proyectos de Big Data en entornos Cloud, con responsabilidad en mantenimiento correctivo y evolutivo, gestión de…
Design, develop, and maintain data pipelines using Databricks on Azure and AWS with Python, PySpark, and Pandas, working within Agile/DevOps teams on client projects.
Senior Data Engineer building and maturing a scalable Azure/Databricks data platform for an accounting and advisory firm in Utrecht, developing PySpark/SQL pipelines, data models, and CI/CD workflows.
Senior Data Engineer in Amsterdam focusing on designing and operating high-quality data pipelines on a Big Data Platform, collaborating with stakeholders, and championing data best practices. Core technologies include Python, PySpark, Airflow, Hadoop, Spark, Kafka, and SQL.
Build and maintain scalable ELT data pipelines using Python, PySpark, Airflow, Hadoop, and Spark for a Big Data Platform.
Senior Data Engineer building scalable data platforms and pipelines using Python, PySpark, and SQL on a lakehouse architecture within VodafoneZiggo's Data & AI team, in a hybrid work setup.
The Principal AI Engineer will lead the design and implementation of agentic AI systems and data platforms within a financial services environment. The role involves building RAG solutions, scalable data pipelines, and AI observability frameworks using technologies like Python, Spark, and Databricks.
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