Senior Data Engineer

Experis to światowy lider rekrutacji specjalistów i kadry zarządzającej w kluczowych obszarach IT.

• Extensive professional experience in backend development.
• Strong experience in Solution Architecture within enterprise environments.
• Hands-on experience with Azure Databricks.
• Strong knowledge of data modeling and data management.
• Extensive experience with Data Quality Controls and data quality management.
• Strong experience with Power BI.
• Advanced DAX skills.
• Experience integrating backend applications, data platforms, and Power BI solutions.
• Proven ability to take technical ownership while remaining hands-on with implementation.
• Experience conducting code and solution reviews and providing technical guidance to other developers.
• Strong problem-solving skills and the ability to address complex technical challenges.
• Proven experience integrating AI functionalities into enterprise data environments or comparable projects.
• Ability to communicate effectively with both technical teams and customers.

• Take technical ownership of backend and data-related solutions within the HEART project.
• Design and implement scalable, reliable, and maintainable backend solutions.
• Work hands-on with Azure Databricks and enterprise data environments.
• Design and maintain effective data models, with initial ownership of data modeling for the EPD topic.
• Define and implement Data Quality Controls and data management processes.
• Ensure data quality, consistency, reliability, and traceability across the data platform.
• Integrate backend services, data platforms, and Power BI solutions.
• Develop and maintain DAX solutions to support reporting and analytical requirements.
• Review technical solutions and code developed by other team members.
• Provide technical guidance and mentoring to developers.
• Act as a technical sparring partner for the customer, helping translate business requirements into robust technical solutions.
• Support the development team in resolving complex technical and architectural challenges.
• Evaluate different technical approaches and recommend suitable solutions based on proven patterns and project experience.
• Contribute directly to development and implementation rather than focusing solely on architecture documentation.
• Ensure solutions follow best practices for scalability, maintainability, performance, and data quality.
• Support the integration of AI functionalities into enterprise data environments where required.