Big Data Engineer (Recommendation) - BytePlus

at  BYTEPLUS PTE LTD

ByteDance will be prioritizing applicants who have a current right to work in Singapore, and do not require ByteDance’s sponsorship of a visa.
Founded in 2012, ByteDance’s mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Helo, and Resso, as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.

QUALIFICATIONS

• B. Sc or higher degree in Computer Science or related fields from accredited and reputable institutions.
• Proficient in developing batch and streaming big data applications using Spark, Flink, Kafka, etc., with a deep understanding of their principles and performance optimization.
• Proficient in programming languages such as Java, Scala, Python, with a solid foundation in computer science and strong coding, system design, and problem-solving skills.
• Familiar with modern big data ecosystem such as Yarn, Kubernetes (K8s), HDFS, Hive, Iceberg/Hudi, HBase, ClickHouse etc.
• Agile, quick self learner, highly self-motivated with a strong sense of product ownership.
• Experience in developing search, recommendation, advertising system or big data platform would be a plus.
• At least 3+ years of relevant experience.
  ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At ByteDance, our mission is to inspire creativity and enrich life. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too

• Responsible for the continuous evolution of the BytePlus Recommendation Product’s data architecture.
• Participate in developing a full life-cycle of ML sample preparation, including data ingestion, validation, transformation, and collaborate with MLEs to provide top-level recommendation solutions to To-B clients.
• Build cutting-edge indexing, feature engineering, and data storage solutions to support efficient machine learning algorithm iteration.
• Develop systematic processes and tools to optimize system costs and enhance overall stability and efficiency.