Python programmer állások II. kerületben (adatbázisszakértő)

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

• Understanding underlying data structure of an ERP system (Microsoft Dynamics 365 Finance & Operations)
• Creating technical specification documents for functional requirements
• Managing existing and creating new automations using Alteryx, Python, SSIS
• Creating reports and dashboards in Power BI

• Komplex adattárház és üzleti intelligencia igények felmérése, megoldások tervezése, megvalósításának szakmai koordinálása, kapcsolattartás az üzleti területekkel.
• Önálló BI megoldások, operatív és vezetői riportok tervezése és fejlesztése.

Ready to fast‑track your career and take on real global challenges? ...

• Maintaining and evolving data structures
• Building new ETL flows and maintaining or improving existing ones
• Designing new data structures and data modelling concepts
• Ensuring data quality by verifying data consistency and accuracy
• Creating data validation methods

• Making architectural decisions and conducting code reviews,
• Mentoring junior developers and leading the team,
• Designing and building scalable AI data pipelines,
• Developing LLMs (Large Language Models) and AI agent systems,
• Efficiently processing large volumes of data using Python,

• Design and build scalable, reusable frameworks and services for data ingestion, transformation, and processing
• Develop and maintain the platform’s core capabilities such as orchestration, data quality validation, and schema management tools

• Managing day-to-day operations of Databricks data pipelines, including monitoring, troubleshooting, and performance tuning
• Maintaining Delta Lake data layers (bronze / silver / gold), ensuring data availability, consistency, and smooth pipeline execution

• Design and implement data fabric architecture across heterogeneous data sources and platforms
• Develop scalable ETL/ELT pipelines for batch and real-time data integration
• Integrate structured and unstructured data from databases, APIs, SaaS applications, data lakes, and enterprise systems

Working on the design, development, and production of advanced digital solutions, ensuring seamless integration with existing infrastructure and adherence to enterprise architecture standards and robust delivery lifecycle processes. This includes leveraging expertise in various digital technologies...

• Ownership of data models and storage architecture behind AI systems across relational, graph, and retrieval-oriented data
• Building and operating APIs, backend services, and pipelines to keep data current and accessible

• Ownership of data engineering features, architecture, and code quality
• Design, implementation, and maintenance of Databricks-based data pipelines and workflows
• Building and optimization of ETL/ELT processes using Apache Spark on Databricks

Performing tasks related to the position....

• Design IT platforms, processes, and organizational structures for evolving Lakehouse on Azure
• Develop sustainable architectures, prototype solutions, and evaluate new technologies to drive innovation

• Designing, implementing, testing, and optimizing reliable, efficient, and high-quality data pipelines
• Automating the extraction, loading and transformation of data from a wide variety of sources
• Developing solutions for the automated acquisition of data from source systems

• Design and maintain enterprise-wide data models including conceptual, logical, and physical layers
• Lead data architecture for core platforms and support cross-value-stream data requirements
• Define data standards, naming conventions, and modelling best practices

• Data ingestion processes from various sources into the Data Lake
• Design, development and optimization of complex data pipelines for reliable data flow
• Build, development and maintenance of frameworks facilitating construction of data pipelines

• 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

• Measuring performance of multi-channel patient recruitment campaigns across clinical trial portfolios
• Optimizing performance of multi-channel patient recruitment campaigns across clinical trial portfolios

• 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