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Principal data engineer • madrid

Última actualización: hace 13 horas
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Principal Data Engineer

Principal Data Engineer

IntelliasEspaña
Hace más de 30 días
Descripción del trabajo

Principal Data Engineer – iGaming Industry

Location : Spain

Tech Stack : Apache Kafka, Python / Scala / Java, PostgreSQL, MySQL, Redis, Flink, ClickHouse, AWS / Azure / GCP

Project Overview :

Join a cutting-edge company in the iGaming industry , developing real-time data pipelines that power mission-critical applications. Our client operates at the intersection of cloud and on-premises infrastructures , delivering high-performance, scalable data solutions that support millions of transactions daily.

As a Principal Data Engineer , you will lead a team of data engineers in designing, developing, and optimizing stream processing architectures , ensuring reliability, scalability, and cost-efficiency. You will collaborate with architecture, product, and engineering teams to deliver innovative data-driven solutions that align with business objectives.

Requirements :

  • 5+ years of hands-on experience in data engineering, specializing in Python, Scala, or Java.
  • Deep expertise in Apache Kafka for real-time data streaming (mandatory).
  • Proficiency in managing and optimizing databases (PostgreSQL, MySQL, MSSQL, Oracle).
  • Experience working with analytical databases and distributed storage solutions .
  • Strong knowledge of cloud platforms (AWS, Azure, Google Cloud) and on-premises data environments .
  • Experience with Flink, Redis, RabbitMQ, Superset, Cube.js, Minio, and Grafana (optional but beneficial).
  • Proven leadership and mentoring skills , with experience guiding a team of engineers.
  • Strong focus on system reliability, scalability, and data integrity best practices .
  • iGaming industry experience is a significant advantage.

Responsibilities :

  • Lead and mentor a team of data engineers, providing technical guidance and architectural oversight.
  • Own end-to-end development of scalable data pipelines and architectures for real-time analytics.
  • Optimize data storage and processing using technologies like Apache Kafka, Redis, and ClickHouse.
  • Migrate selected cloud-based solutions to on-premises environments, optimizing for performance and cost.
  • Collaborate with stakeholders to define data strategies and ensure business alignment.
  • Implement best practices for system reliability, fault tolerance, and high availability .
  • Stay up to date with emerging technologies to drive innovation in data processing and analytics.