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Senior Data Engineer - 2026 World Cup

Global Sports Data Systems
New York
Estimated Salary
USD 145.000 – USD 190.000
Live Update
22 Mei 2026
Deadline
22 Mei 2027

Job Description

Are you ready to build the technological backbone for one of the world's most significant sporting events? Global Sports Data Systems is seeking a visionary Senior Data Engineer to join our elite team preparing for the historic 2026 World Cup. In this pivotal role, you will architect and manage scalable data pipelines that process millions of fan interactions, player performance metrics, and real-time analytics across the United States, Canada, and Mexico.

We are looking for a problem solver who thrives in high-pressure environments and wants to leave a legacy in sports technology. If you have a passion for big data and a desire to shape the future of global sports, this is your opportunity to join the vanguard.

Responsibilities

  • Design, build, and maintain robust ETL pipelines to handle high-velocity data streams from stadium sensors and global broadcast feeds.
  • Collaborate with cross-functional teams including data scientists, product managers, and event operations to drive data-informed decision-making.
  • Ensure data integrity, security, and compliance with international data privacy regulations (GDPR/CCPA).
  • Optimize database performance and implement caching strategies to support real-time dashboards for stakeholders.
  • Implement cloud-native architectures on AWS/Azure to support multi-region deployment for the 2026 event.

Qualifications

  • Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s preferred).
  • 5+ years of experience in data engineering, with specific experience in big data technologies (Spark, Kafka, Hadoop).
  • Proficiency in Python, SQL, and NoSQL databases (MongoDB, Cassandra).
  • Strong understanding of distributed systems and cloud computing platforms (AWS/GCP).
  • Experience working on large-scale, high-availability systems or live event technology.

Required Skills

Python SQL AWS Spark Kafka Hadoop Data Pipeline Big Data ETL Cloud Computing

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