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Senior, highly specialized big-data role with many mandatory skills — low applicant competition.
High domain specificity (distributed systems, Spark, Kafka, streaming) limits cross-industry transferability.
Multiple explicit years and mandatory deep big-data and architecture experience enforce strict filters.
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Own and define the technical vision and long-term strategy for a large-scale data platform and pipeline architecture processing billions of daily events.
Design, scale, and optimize distributed data processing systems with a focus on high availability, performance, and cost efficiency.
Lead architecture decisions across multiple teams and troubleshoot large-scale performance and reliability issues in production environments.
Minimum 12 years of experience in software, data, or distributed systems engineering.
At least 6 years designing and supporting Apache Spark data processing solutions and large-scale data pipelines.
Minimum 4 years experience administering Apache Kafka and designing event-driven or streaming data architectures.
Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field.
Proven technical leadership with ownership of architecture and multi-team engineering initiatives involving big data pipelines.
Deep expertise in distributed systems, large-scale data processing, and event-driven architectures at enterprise scale.
Experience mentoring engineers and driving engineering standards, governance, and best practices for scalable data platforms.