Data specialist állások, munkák it programozás, fejlesztés területen

• Co-design and implement QA workflows using statistical diagnostics, validation rules, and anomaly detection techniques
• Apply AI/machine learning tools to identify inconsistencies, missing values, or suspicious patterns in large-scale asset datasets

• Build Data Pipelines: Design, build, and maintain scalable data pipelines for ingesting, transforming, and serving financial and operational data.
• Work with ERP Data: Integrate and model data from ERP systems and finance tools, including accounting, controlling, procurement, revenue, and cost data.

• You support data analytics activities using KNIME, Excel and other analytical tools.
• You analyse large datasets, identify patterns, anomalies and potential risks, and help translate data into meaningful audit insights.

• Az IT termék vagy alkalmazás backlogjának kezelése és priorizálása
• Üzleti és technikai követelmények összegyűjtése és pontosítása
• User story-k, functional specificationök és acceptance criteria-k készítése
• Folyamatos együttműködés fejlesztői és tesztelői csapatokkal

• Support of the modules MM, PP, and EWM
• Management of various add-ins within the SAP modules
• Definition and documentation of business processes in MM, PP, and EWM
• Support for Key Users in the departments at our sites and subsidiaries

Manipulating and analyzing complex, high-volume and high-dimensional data to gain insights by using appropriate data analysis techniques, algorithms and programming languages in close collaboration with subject matter experts...

• Folyamatos kapcsolattartás az üzleti terület IT képviselőivel
• Rendszerszervezési tudást igénylő szakmai feladatok végrehajtása és dokumentálása
• Komplex szakértői közreműködés dokumentációk és folyamatábrák készítésében

• 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

• Definition and improvement of metrics representing product performance and operations efficiency
• Data collection from multiple sources ensuring accuracy and completeness
• Data cleaning, organization, and transformation into usable formats for analysis

• Executing system data maintenance and administrative activities in Cornerstone and all relevant Learning management systems
• Providing non-systems related Learning process support

Design, develop, and maintain AI agents for manufacturing use cases (e.g. quality inspection, anomaly detection, process optimisation), using machine learning, deep learning, GenAI, rules, and reasoning....

• Elicitation of business needs through stakeholder workshops
• Documentation of user requirements specifications and functional specifications
• Development and maintenance of requirements traceability matrix
• Impact assessment of proposed system changes on validated state

• Design, develop, and implement complex, scalable Lakehouse solutions on the Databricks platform across major cloud environments
• Design, build, and optimize modular data pipelines using Delta Lake to ensure data efficiency, reliability, and quality

• Design/build AI-ready Lakehouse architectures (Databricks, Fabric) and data pipelines for analytics/ML
• Develop financial data models (Data Vault 2.0) with governance (Purview, Unity Catalog)
• Architect AI infrastructure: Cognitive Search, RAG, vector DBs, knowledge graphs for agentic AI

Tailor the SAP-RPT model family to the unique complexities of real-world relational enterprise data, enabling models to navigate and reason over intricate schemas and logic...

Design, train, and evaluate machine learning models for manufacturing use cases such as quality prediction, anomaly detection, predictive maintenance, and process optimisation, covering the full ML lifecycle from problem definition to performance tuning....

• Implementation and maintenance of large-scale data pipelines and the Azure/Databricks data platform
• Building robust, scalable pipelines using Azure Databricks (PySpark / SQL)
• Integration of data ingestion, processing, transformation, and storage

• Design of Azure cloud infrastructure to support Databricks and data platform workloads
• Implementation and management of Azure cloud infrastructure
• Building and maintaining Azure resources including Resource Groups, VNets, Key Vaults, Storage Accounts, and Azure Active Directory integrations

• Ensure accuracy, reliability, performance, and compliance of AI models and systems across development, testing, deployment, operational use, and feature enhancement
• Develop and execute test plans and validation frameworks for AI products, ML solutions, LLM applications, and agentic systems

• Dashboard development with Power BI to visualize sustainability data and metrics
• Data integration from internal databases, external APIs, and third-party platforms
• Client reporting collaboration with sustainability team for customized visual analytics