Python állások, munkák adatbázisszakértő kategóriában

• Define and further develop the Snowflake platform roadmap in alignment with enterprise data strategy and business priorities
• Establish the platform vision and manage priorities, demand intake and business requirements to maximize business value and support strategic objectives

• An opportunity to build the foundation for next-generation products
• The option to write a master thesis following an internship or working student position
• Hands-on work on intrinsic, extrinsic, and multi-camera calibration for ZEISS use cases

• 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

• Lead the design and implementation of scalable data pipelines and Lakehouse architectures
• Act as the design authority
• Solve the hardest technical challenges including optimizing high-load streaming pipelines, debugging complex Spark jobs, and designing generic frameworks

• Technical leadership and mentoring of data engineers
• Technical oversight for Databricks solutions
• Design and implementation of scalable ETL/ELT pipelines using Databricks, Python/PySpark, SQL, and Delta Lake

• Own the end-to-end forecasting and price-elasticity models at the core of our pricing engine — from feature and model research through evaluation, deployment, and continuous monitoring
• Scale the forecasting architecture to meet growing demand for granular, product-level forecasts

• Evaluate, configure, and maintain the AI development tool stack (GitHub Copilot, custom agents, local LLMs).
• Standardise how AI agents interact and integrate with our internal tools, services, and development workflows with MCP.

• The data platform end to end: ingestion, storage, and the query paths that keep millions of records fast on screen. You design it, you build it, you run it in production.
• The platform layer, directly with the CTO: infrastructure, deployments, and the path to production for the whole team.

• Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e. g., SEM, TEM, EBSD).
• Learning representations that are robust to scale changes, modality shifts, and domain differences across instruments and laboratories.

• Build and maintain dataset ingestion, cleaning, and versioning pipelines
• Design and operate labeling workflows including quality control and inter-annotator agreement tracking
• Own dataset testing including coverage analysis, class balance, leakage detection, and dataset documentation

take responsibility as Tech Lead Search for the technical quality and architecture of our product retrieval and embedding capabilities based on OpenSearch....

• Skálázható cloud-native adatarchitektúrák tervezése Databricks vagy MS Fabric platformokra
• Adatmodellek, sémák és tárolási minták kialakítása (lakehouse, Delta, Parquet)
• Batch és streaming adatok betöltési pipeline-jainak megtervezése
• ETL/ELT folyamatok és transzformációs stratégia meghatározása

• Adatforrások integrálása a vállalat új Data Lakehouse platformján
• Adatbetöltési folyamatok fejlesztése a Data Lakehouse platformon
• Skálázható adatfeldolgozó folyamatok fejlesztése és optimalizálása
• Adattranszformációk fejlesztése és optimalizálása

Design, build, and maintain scalable data platforms and data & AI solutions tailored to business needs, including the development of governance structures and access control concepts...

• 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.

• 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