Senior Data Engineer - Data Engineering

Luxoft DXC; Kraków; On-site; PLN 180,000 - 320,000; Full time

Job summary

Luxoft DXC is seeking an experienced Senior Data Engineer to join a project focused on observability and embedded analytics for an automotive client. You will transform logs, events, measurements and telemetry into trusted datasets and engineering insights, building production data pipelines and analysis-ready data for faster troubleshooting and reliability analysis.

You will design data models, process high-volume time-series data, and integrate data from embedded and cloud sources.

Responsibilities

• 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.

Job description

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.