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Mid-level, metro location, generalist data-engineer skillset, and broad tech requirements heighten candidate competition.
Core big-data, Spark, Kafka, and AWS skills are highly transferable across industries.
Explicit 5+ years plus mandatory Spark, Kafka, AWS, SQL and backend data engineering skills increase filter strictness.
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Develop and maintain scalable backend services and distributed data processing pipelines for global map data processing using Java and Python.
Lead architectural design and implement automated error detection and correction in mapping data, integrating with navigation and autonomous driving products.
Ensure end-to-end delivery including development, testing, performance optimization, production support, and collaboration with cross-functional agile teams.
5+ years experience in Big Data/Data Engineering with strong proficiency in Java and Python.
Experience with Apache Spark, AWS EMR, event-driven architecture using Apache Kafka, and cloud-native services on AWS (S3, Lambda, EMR).
Strong knowledge of OOP, concurrency, JVM fundamentals, SQL (preferably PostgreSQL), and production ready backend API design.
Work Experience Required: 5+ years in relevant technology stack and domain.
Experienced in building and operating large-scale, distributed data pipelines and backend services for geo-spatial or map data processing.
Demonstrated ability to contribute to solution architecture, implement testing strategies, and maintain code quality in agile environments.
Skilled in cloud-native technologies, CI/CD pipelines, monitoring/observability, with strong debugging and performance optimization capabilities.