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Global brand plus mid-level, popular data/backend title and broad skillset create high applicant competition.
Core skills like Spark, cloud, data pipelines, and APIs are broadly transferable across industries.
Explicit 4–6 years plus mandatory Spark, cloud, lakehouse, backend and data engineering skills enforce strict filtering.
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Design, develop, and maintain scalable, robust data pipelines and backend systems for large-scale metadata solutions in video, audio, automotive, and sports domains.
Architect and implement high-performance, reliable, and scalable data processing systems with fast refresh cycles using distributed data technologies and cloud platforms.
Ensure data governance and quality while collaborating cross-functionally to translate business requirements into technical solutions; mentor junior engineers to foster continuous improvement.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
4 to 6 years of professional experience in backend and data engineering managing large-scale datasets and real-time event processing.
Strong programming skills in Python, Java, or Scala; experience with distributed data systems like Spark or Flink.
Experience with cloud platforms (AWS, Azure, GCP), distributed storage (HDFS, S3), databases (NoSQL and relational), and building scalable RESTful APIs.
Experienced in designing and optimizing high throughput, low latency, resilient backend architectures within large-scale data environments.
Demonstrates strong technical leadership by mentoring peers and conducting design/code reviews to uphold quality standards.
Familiar with Agile development, CI/CD practices, and modern data platform concepts including Lakehouse architectures and event-driven systems.