Data engineer állások Németországban (rendszerintegrátor)

• Develop and optimize our job data crawling pipeline, ensuring high-quality job data acquisition.
• Implement and refine import mapping, improving data accuracy and classification.
• Support the integration of external data sources via APIs or scraping frameworks.

• 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

• Design, implement and maintain our development platform and cloud environments using Infrastructure as Code
• Build and maintain tools and services to enable and accelerate our development teams and improve the maintainability and operability of our infrastructure

Analysis of avionics system design/requirements requirements Develop executable test plans which address technical specification verification requirements Definition and adaptation of design requirements to mission data generation, incl. workflow definition Support tests evaluation of system...

• Analyse the external and internal systems' needs, missions needs, and customer requirements
• Define information and data that shall be transferred within the system and with external systems based on NATO standards

Architect the ML Platform: Design, implement, and maintain a robust MLOps infrastructure that enables researchers to move seamlessly from local experimentation to global production....

• Development of firmware for MicroTCA-based motion controller system
• Contributing to system architecture and specifications of firmware modules for scientific data acquisition systems and photon science experiments
• FPGA firmware design using VHDL (for AMD/Xilinx FPGAs)

• Work closely with product owners and stakeholders to analyze business needs and integrate them into the CRM system
• Define and enforce CRM development standards based on best practices and governance models
• Configure the system, workflows, automation, and integrate it with third-party systems

• Model Research: Conduct research on possible AI models for our use case
• Data Preparation: Assist in curating and preparing datasets of from our database of known errors
• Model Training: Training and fine-tuning the AI agent, ensuring it learns how to accurately detect errors and suggest fixes