Data engineer állások, munkák programozó, fejlesztő kategóriában

• Keep data moving: Build, run, and refine data pipelines and workflows (Python, Airflow/Cloud Composer) so they're reliable, fast, and easy to change
• Shape our data models: Use BigQueryand Cloud Spanner to design wellstructured, highperformance datasets for analytics and machinelearning use cases

Lead the local team in developing new applications, ensuring that each phase, from design to coding, is executed with precision and quality. Oversee the installation and configuration of applications in various environments, ensuring a smooth and seamless transition for end users. Continuously...

Developing and maintaining data pipelines in Azure Databricks Writing transformation logic using Python and SQL Supporting data migrations and platform improvements Working within a structured, company-level Databricks framework Collaborating in a Scrum-based setup (2-week sprints, Jira, daily...

Build, implement, and scale machine learning models to detect and prevent fraud Lead initiatives in the Risk & Fraud space, setting technical direction and influencing team strategy Leverage SQL and Python to transform and prepare large-scale datasets for analysis and modeling Deliver high-quality,...

Implement and maintain large‑scale data pipelines and the Azure/Databricks data platform in close collaboration with the Architect, contributing to design discussions and proposals. Build robust, scalable pipelines using Azure Databricks (PySpark / SQL), integrating data ingestion, processing,...

Work closely with customers to analyze business needs and translate them into technical requirements. Participate in pre-sales discussions, helping shape tailored technical solutions. Collaborate with Sales and Customer Success teams to ensure smooth project execution and client satisfaction....

• Contribute to pioneering technical advances to the field of foundation models on structured data, as well as exploringthe benef world models for solving business problems.
• Execute scientific experiments on internal as well as public data.