Data Engineer - BigData Development

Luxoft; Kraków; Presencial; PLN 240.000 - 360.000; Jornada completa

Descripción de la vacante

Luxoft in Kraków is seeking an experienced Senior Data Engineer to join an observability and embedded analytics project for an automotive client. You will build production-grade pipelines transforming logs, events, measurements and telemetry into trusted datasets and engineering insights.

You will design data models, develop pipelines, ensure data quality and create analysis-ready datasets to support troubleshooting, reliability analysis and product-quality follow-up.

Formación

• Strong experience developing and operating production-grade data pipelines.
• Hands-on Python and SQL skills.
• Knowledge of data transformation and data modelling.
• Experience with cloud-based data solutions.
• Experience with data orchestration and automated workflows.
• Experience with APIs, event data and logs.
• Experience with data quality and pipeline monitoring.
• Experience with high-volume time-series and telemetry data.

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.

Conocimientos

Data pipelines

Python

SQL

Data transformation

Data modelling

Cloud data

Data orchestration

APIs & logs

Data quality

Time-series

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.

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.

Mandatory Skills Description:

• Strong experience developing and operating production-grade data pipelines.
• Strong hands-on Python and SQL skills.
• Strong knowledge of data transformation and data modelling.
• Experience with cloud-based data solutions.
• Experience with data orchestration and automated workflows.
• Experience working with APIs, event data and logs.
• Experience with data quality and pipeline monitoring.
• Experience working with high-volume time-series and/or telemetry data.

Nice-to-Have Skills Description:

• Automotive or connected-car experience.
• Experience working with data from embedded systems or devices.
• Experience supporting troubleshooting, reliability or product-quality analytics.
• Experience with data visualisation and dashboards.
• Mobile analytics experience.
• Knowledge of data lineage and data governance.