Senior Data Engineer - Data Platform Engineering

Luxoft DXC; Kraków; Presencial; PLN 180 000 - 260 000; Tempo integral

Resumo da oferta

Luxoft DXC is seeking a Senior Data Engineer specializing in Data Platform Engineering to join a project focused on observability and embedded analytics for a leading automotive client. The role concentrates on building a scalable data foundation that transforms logs, events, measurements, and telemetry into trusted data products.

You will design and engineer the data platform, develop robust data pipelines, shared models, and data quality monitoring to ensure reliable, reusable datasets for

Responsabilidades

• Design and implement the target data architecture and production data foundation.
• Build and maintain robust, automated data pipelines and transformations.
• Develop shared data models, schemas and analysis-ready datasets.
• Implement data orchestration, data-quality controls, monitoring and lineage.
• Integrate data from embedded, cloud, test and application systems using APIs and other data interfaces.
• Improve platform reliability, scalability, performance and maintainability.
• Support analytical use cases while keeping the underlying data platform reusable.
• Produce operational documentation and support knowledge transfer to Client teams

Descrição da oferta de emprego

We are looking for a Senior Data Engineer specialising in Data Platform Engineering to join a project focused on observability and embedded analytics for a leading automotive Client.

The project aims to establish a scalable and maintainable data foundation that transforms logs, events, measurements and telemetry from embedded, cloud, test and application systems into trusted and reusable data products.

In this role, you will focus on the architecture and engineering of the data platform, including pipelines, orchestration, common data models, data quality and operational monitoring.

• Design and implement the target data architecture and production data foundation.
• Build and maintain robust, automated data pipelines and transformations.
• Develop shared data models, schemas and analysis-ready datasets.
• Implement data orchestration, data-quality controls, monitoring and lineage.
• Integrate data from embedded, cloud, test and application systems using APIs and other data interfaces.
• Improve platform reliability, scalability, performance and maintainability.
• Support analytical use cases while keeping the underlying data platform reusable.
• Produce operational documentation and support knowledge transfer to Client teams