Senior Data Engineer - BigData Development

Luxoft DXC; Kraków; Presencial; PLN 180.000 - 250.000; Jornada completa

Descripción de la vacante

Luxoft DXC is seeking an experienced Senior Data Engineer to join a project in observability and embedded analytics for a leading automotive client. You will design, build and maintain production data pipelines and transformations, turning logs, events, telemetry and measurements from embedded, cloud, test and application systems into trusted datasets.

You will develop data models, ensure data quality, support dashboards and analytical use cases, and document delivered solutions to enable

Responsabilidades

• Design, build and maintain production-grade data pipelines and transformations.
• Develop data models, schemas and curated datasets for different engineering use cases.
• Process logs, events, telemetry and high-volume time-series data.
• Integrate data from embedded, cloud, test and application sources using APIs and other data interfaces.
• Implement automated data-quality checks and pipeline monitoring.
• Build analysis-ready datasets supporting troubleshooting, reliability and product-quality analysis.
• Improve pipeline reliability, performance and maintainability.
• Support dashboards and analytical use cases.
• Document delivered solutions and support knowledge transfer.

Descripción del empleo

We are looking for an experienced Senior Data Engineer to join a project within observability and embedded analytics for a leading automotive Client.

The project focuses on transforming logs, events, measurements and telemetry from embedded, cloud, test and application systems into trusted and reusable datasets and engineering insights.

You will have a hands-on role in building production data pipelines, transformations and analysis-ready datasets that support faster troubleshooting, reliability analysis, product-quality follow-up and improved understanding of platform performance in real-world conditions.

• Design, build and maintain production-grade data pipelines and transformations.
• Develop data models, schemas and curated datasets for different engineering use cases.
• Process logs, events, telemetry and high-volume time-series data.
• Integrate data from embedded, cloud, test and application sources using APIs and other data interfaces.
• Implement automated data-quality checks and pipeline monitoring.
• Build analysis-ready datasets supporting troubleshooting, reliability and product-quality analysis.
• Improve pipeline reliability, performance and maintainability.
• Support dashboards and analytical use cases.
• Document delivered solutions and support knowledge transfer.