Data Architect | Data Engineer Internship en Madrid, España
Capgemini
PresencialTrabaja desde la oficina
Madrid, España
Profile we are looking for
We are looking for people pursuing a Master's degree in Big Data or Computer Science, with a high level of English (C1), motivated to develop in a technological and dynamic environment.
General Objectives
Understand and experiment with the technologies, tools, and current processes used in Data Engineering, including data ingestion, transformation, and storage.
Participate in the critical evaluation of the quality, efficiency, and security of data processing within a corporate environment.
Learn best practices for the responsible, secure, and efficient integration of data pipelines and systems in real products and services.
Data Ingestion, Processing, and Transformation
Design, evaluation, and use of data pipelines for specific tasks, such as ingestion from APIs, databases, files, or external services.
Development of data cleaning, validation, transformation, and normalization processes using Python, SQL, or other processing tools.
Comparison of performance and efficiency between different processing methods and technologies (e.g., relational databases vs. NoSQL, batch processes vs. streaming).
Solution Development and Integration
Design and construction of data pipelines to support use cases such as analytics, reporting, or consumption by internal applications.
Use of orchestration and automation tools (such as Airflow, Prefect, cron, or other internal orchestrators) to manage data flows.
Development of integration solutions between different data sources, including APIs, corporate databases, and external systems.
Integration of data pipelines into backend services developed in Python or other languages, as well as into visualization dashboards or applications (e.g., Power BI, Streamlit, or internal dashboards).
Data Evaluation, Security, and Governance
Design and execution of data quality tests (validations, automated checks, monitoring of integrity and consistency).
Review of ethical and security aspects in data handling: privacy, treatment of sensitive information, permissions, and internal governance.
Documentation of processes, datasets, results, and learnings in a clear and structured manner for the engineering and analytics team.
Perfil que buscamos
Buscamos personas cursando el Máster en Big Data o Informática, con un alto nivel de inglés (C1), motivadas por desarrollarse en un entorno tecnológico y dinámico.
Objetivos Generales
Comprender y experimentar con las tecnologías, herramientas y procesos actuales utilizados en la Ingeniería de Datos, incluyendo ingestión, transformación, modelado y almacenamiento de datos.
Participar en la evaluación crítica de la calidad, eficiencia y seguridad de los procesos de tratamiento de datos dentro de un entorno corporativo.
Aprender buenas prácticas para la integración responsable, segura y eficiente de pipelines y sistemas de datos en productos y servicios reales.
Ingestión, Procesamiento y Transformación de Datos
Diseño, evaluación y uso de pipelines de datos para tareas específicas, como ingestión desde APIs, bases de datos, ficheros o servicios externos.
Desarrollo de procesos de limpieza, validación, transformación y normalización de datos mediante Python, SQL u otras herramientas de procesamiento.
Comparativa de rendimiento y eficiencia entre diferentes métodos y tecnologías de procesamiento (por ejemplo, bases de datos relacionales vs. NoSQL, procesos batch vs. streaming).
Desarrollo e Integración de Soluciones
Diseño y construcción de pipelines de datos para soportar casos de uso como analítica, reporting o consumo por aplicaciones internas.
Uso de herramientas de orquestación y automatización (como Airflow, Prefect, cron, u orquestadores internos) para gestionar flujos de datos.
Desarrollo de soluciones de integración entre distintas fuentes de datos, incluyendo APIs, bases de datos corporativas y sistemas externos.
Integración de pipelines de datos en servicios backend desarrollados en Python u otros lenguajes, así como en paneles o aplicaciones de visualización (por ejemplo, Power BI, Streamlit o dashboards internos).
Evaluación, Seguridad y Gobernanza del Dato
Diseño y ejecución de tests de calidad de datos (validaciones, controles automáticos, seguimiento de integridad y consistencia).
Revisión de aspectos éticos y de seguridad en el manejo del dato: privacidad, tratamiento de información sensible, permisos y gobernanza interna.
Documentación de procesos, datasets, resultados y aprendizajes de forma clara y estructurada para el equipo de ingeniería y analítica.
This job was automatically translated to English, .
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