Senior/Princial Big Data Engineer

Duties & Responsibilities:  

• Lead end-to-end design of large-scale or multi-domain data processing systems. Evaluate and select technologies, processing models, and architectural standards aligned with the organization’s strategic direction. 

• Develop technical standards for data pipelines, metadata management, logging, monitoring, and data organization. Ensure team compliance through design reviews, code reviews, and technical guidance. 

• Analyze, assess, and optimize compute and storage costs for large-scale data platforms. Design partitioning, indexing, caching strategies, and incremental processing mechanisms to maximize overall system performance. 

• Provide training and technical leadership for junior and mid-level engineers, while resolving complex technical issues faced by the team. Conduct reviews of critical components to ensure system quality and stability. 

• Collaborate with Security and Data Governance teams to ensure compliance with security, data lineage, and audit requirements. Establish and monitor data quality standards for critical projects or domains. 

Job Requirement:  

• Bachelor’s or Master’s degree in Machine Learning, Artificial Intelligence, Computer Science, Computer Engineering, Information Technology, Data Science, or a related field. 

• Preference for graduates from reputable universities ranked within the top 1000 of the QS World University Rankings, or U.S. research universities classified as “R1: Doctoral Universities – Very high research activity” under the Carnegie Classification of Institutions of Higher Education. 

• Possession of internationally recognized data-related professional certifications or advanced certifications in distributed systems. 

• At least 6 years of hands-on experience developing Big Data systems, including a minimum of 3–4 years in senior or principal engineer roles at technology companies or enterprises. 

• Experience in implementing data processing platform tools on bare-metal infrastructure or cloud-native environments and integrating them into real-world products. 

• Proven experience in designing complex data models that are reusable across multiple projects. 

• Experience in designing high-performance, stable real-time data processing pipelines. 

• Served as a mentor or technical lead in large-scale digital transformation projects leveraging Big Data. 

• Strong understanding and application of data management standards in enterprise data products.