Junior python developer állások, munkák adatbázisszakértő kategóriában

• Building, operating and improving production data pipelines using Python, Airflow, Kubernetes and related technologies
• Designing scalable data pipelines and data models for growing data volumes and processing needs

• Adat töltések fejlesztése
• Mesterséges intelligencia modellek fejlesztésében részvétel
• Alkalmazás fejlesztésekben részvétel

• Operate, govern, secure, and continuously improve the Azure Databricks platform, ensuring reliability, scalability, security, and cost efficiency.
• Manage Databricks workspaces, environments, account-level settings, identities, and platform access controls.

• Managing complex database systems within a global technology ecosystem
• Building and managing database hosting capabilities using Infrastructure as Code for all platform needs
• Building database infrastructure platforms with developers and providing guidance and tooling to handle distributed systems

• Establish data feeds with custodian banks, data vendors, and other service providers
• Support senior leadership, clients, and the broader Data Team in executing analyses and providing data-driven answers to questions
• Fully automate and centralize existing data integration processes

• Support the global Finance & Controlling team of BU Audio in carrying out analyses and reports of relevant key figures
• Develop, optimize and maintain Power BI dashboards for KPIs such as sales, order intake, profitability and headcount, including data preparation, modelling and visualization.

Customer models, end to end. Turn a planning problem, with its capacities, costs, lead times and shift plans, into a model whose answer a plant manager acts on. That includes the data it runs on: ERP and Excel exports, and catching the numbers that cannot be right before the customer does....

Drive Architectural Strategy & Scalability: Partner across Data Science, Platform Engineering, and Product teams to design the technical architecture, data pipelines, and orchestration systems required for high-traffic AI services. Ensure the ecosystem supports highly available, low-latency...

• Discussing and clarifying requirements with our internal domain experts
• Breaking requirements down into manageable work packages, estimating effort, and identifying technical dependencies
• Designing and developing algorithms that span and are optimized across various layers of our software stack

• Analyze quality, usage, and diagnostic data from customer vehicles in the field
• Identify product issues, usage challenges, and opportunities to improve the customer experience
• Gain insights from various data sources, including vehicle diagnostics, customer complaints, and user behavior data

• Analyze current project, volume, and aggregate planning processes.
• Identify and evaluate existing data sources, interfaces, and planning methodologies.
• Research state-of-the-art approaches in forecasting, data analytics, machine learning, and artificial intelligence.

• Develop and Implement: Build and maintain platform solutions that improve the reliability, security, and scalability of our Database Platform infrastructure.
• Operational Excellence: Apply cloud infrastructure, networking, and CI/CD best practices to support our operational data storage solutions.

• Managing and migrating ETL pipelines between various source systems
• Validating pipeline build success and output data quality
• Communicating with business stakeholders and SME's regarding their respective data domain

• Conducting a comprehensive literature review of state-of-the-art end-to-end autonomous driving approaches and uncertainty estimation methods
• Evaluating and selecting suitable uncertainty estimation techniques for integration into end-to-end autonomous driving systems

Where you belong: Where you belong: ...

• Drive pioneering technical advances to the field of foundation models on structured data, as well as exploring world models for solving business problems.
• Expand the horizon of what is possible by bringing together rich semantic context with large scale business data

• Data pipeline development
• Data integration
• Data warehousing
• Data modelling
• Data quality and governance
• Performance optimization
• Collaboration with data scientists and analysts
• Technology selection
• Security and compliance
• Monitoring and troubleshooting

• Build agent loops and orchestration: tool use, structured outputs, business-rule enforcement, graceful failure
• Build the integration layer (via MCP) that lets agents securely call backend systems and business APIs
• Render model output into live, streamed, accessible UI via generative-UI protocols

• Build agent loops and orchestration: tool use, structured outputs, business-rule enforcement, graceful failure
• Build the integration layer (via MCP) that lets agents securely call backend systems and business APIs
• Render model output into live, streamed, accessible UI via generative-UI protocols

• Design and implement pipelines to ingest, clean, and transform data into Data Lakes and Data Warehouses
• Active monitoring and optimization of pipelines for performance and cost
• Configuration of pipeline deployments and management of environment settings using IaC tools and CI/CD