Senior Data Engineer

JR Spain; Valladolid; Presencial; EUR 50.000 - 70.000; Jornada completa

Descripción de la vacante

Una empresa de consultoría digital en Valladolid busca un Senior Data Engineer para diseñar y construir soluciones de datos escalables. El candidato ideal tiene más de 6 años de experiencia en Python, conocimiento profundo en plataformas de datos en la nube y es capaz de trabajar en equipos distribuidos. Se ofrece un entorno de trabajo inclusivo y oportunidades para mentoring y desarrollo profesional.

Formación

• 6+ años en un rol de desarrollador senior utilizando Python.
• Experiencia en construcción de pipelines de procesamiento de datos.
• Dominio del inglés hablado y escrito.

Responsabilidades

• Diseñar, construir y mantener plataformas y pipelines de datos.
• Integrar fuentes de datos y optimizar el procesamiento de datos.
• Mentorar a nuevos ingenieros.

Conocimientos

Python

Scala

Spark

SQL

AWS

Docker

Kubernetes

Git

Herramientas

DynamoDB

S3

Apache Airflow

AWS Glue

Descripción del empleo

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For more than 20 years, our global network of passionate technologists and pioneering craftspeople has delivered cutting-edge technology and game-changing consulting to companies on the brink of AI driven digital transformation. Since 2001, we have grown into a full service digital consulting company with 5500+ professionals working on a worldwide ambition. Driven by the desire to make a difference, we keep innovating. Fuelling the growth of our company with our knowledge worker culture. When teaming up with Xebia, expect in-depth expertise based on an authentic, value-led, and high quality way of working that inspires all we do.

At Xebia, we put ‘People First’—committed to attracting diverse talent and fostering an inclusive, respectful workplace where everyone is valued for their contributions. We welcome all individuals and evaluate solely on the quality of their work and teamwork.

About the Role

As a Senior Data Engineer at Xebia, you will work closely with engineering, product, and data teams to deliver our clients scalable and robust data solutions. Your key responsibilities will include designing, building, and maintaining data platforms and pipelines and mentoring new engineers.

Responsibilities:

• Work with various clients globally, delivering software systems and best practices for scalable and robust solutions.
• Engineer data platforms for scale, performance, reliability, and security.
• Design and build big data streaming capabilities.
• Integrate data sources and optimize data processing.
• Develop infrastructure in cooperation with Platform & Reliability engineers.
• Work alongside analysts to enhance the data experience.
• Onboard and mentor new engineers for clients and internal teams.

Requirements:

Basics:

• 6+ years in a senior developer role using Python, with hands-on experience in building data processing pipelines.
• Proven ability to work independently and effectively in a distributed team.
• Strong proficiency in programming languages such as Python and/or Scala.
• Expertise in data processing frameworks and libraries (e.g., Spark, SQL).
• Strong expertise in cloud data platforms/warehouses like Databricks or Snowflake.
• Experience with DynamoDB, S3/Athena.
• In-depth knowledge of database systems (relational and NoSQL), data modeling, and data warehousing concepts.
• Proficiency in designing and implementing ETL/ELT processes and data integration workflows using tools like Apache Airflow, AWS Glue.
• Extensive experience in big data engineering on a terabyte scale, including streaming technologies and near-real-time processing.
• Strong experience with cloud technologies and data pipelines, specially with AWS.
• Proficiency with Docker and hands-on experience with Kubernetes.
• Experience working with VCS like Git.
• Excellent command of oral and written English.
• Ability to work with different stakeholders and drive consensus within the team.

Recommended:

• Understanding of big data and DevOps technologies (Kafka, Spark, Helm, Terraform).
• Experience in CI/CD for the data environment.
• Experience in testing for data processing.
• ML models operationalization (e.g., in Docker, Kubernetes).