Data Engineer, Marketplace Intelligence & Data (2027 Graduate)
Posted
Summary
Data Engineer (2027 graduate intake) at Shopee in Singapore building data warehouses, real-time streaming and OLAP analytics over massive marketplace data, and exploring LLM/agent/RAG-driven data development and governance. Stack spans Java/Python/SQL plus Hadoop, Spark, Flink, Kafka, ClickHouse.
Job Description:
Build data warehouses, real-time computing systems, and statistical analytics solutions for massive volumes of business data. Responsibilities include clarifying data logic, developing data tools, and optimizing workflows. Participate in business performance analysis and provide data-driven insights and analytical methodologies to support product iteration. Contribute to the development of big data service platforms, providing data services for online business scenarios such as querying, estimation, computation, and configuration. Apply technologies such as stream processing and OLAP engines to modernize traditional business processes, improve the efficiency of data processing and utilization, and empower the business with deeper data insights. Explore the application of AI technologies-including large language models (LLMs), agents, and retrieval-augmented generation (RAG)-in the data domain. Promote AI-driven data development and governance practices to enhance the intelligence and automation of data management. Requirements:
Bachelor's degree or above in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Mathematics, Statistics, or a related field. Strong foundation in data structures, algorithms, operating systems, computer networks, databases, and other core computer science concepts. Proficiency in at least one programming language, such as Java, Python, Go, C, or C++. Strong SQL development and performance optimization skills, with familiarity with relational databases such as MySQL and PostgreSQL. Familiarity with one or more big data ecosystem technologies, such as Hadoop, Hive, Spark, Flink, Kafka, HBase, ClickHouse, or Elasticsearch. Experience in data warehouse development, data analysis, or machine learning is preferred. Strong engineering skills and problem-solving abilities, with the capability to independently analyze and resolve complex data-related challenges. A strong interest in AI technologies and data intelligence, along with a commitment to continuous learning.
Build data warehouses, real-time computing systems, and statistical analytics solutions for massive volumes of business data. Responsibilities include clarifying data logic, developing data tools, and optimizing workflows. Participate in business performance analysis and provide data-driven insights and analytical methodologies to support product iteration. Contribute to the development of big data service platforms, providing data services for online business scenarios such as querying, estimation, computation, and configuration. Apply technologies such as stream processing and OLAP engines to modernize traditional business processes, improve the efficiency of data processing and utilization, and empower the business with deeper data insights. Explore the application of AI technologies-including large language models (LLMs), agents, and retrieval-augmented generation (RAG)-in the data domain. Promote AI-driven data development and governance practices to enhance the intelligence and automation of data management. Requirements:
Bachelor's degree or above in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Mathematics, Statistics, or a related field. Strong foundation in data structures, algorithms, operating systems, computer networks, databases, and other core computer science concepts. Proficiency in at least one programming language, such as Java, Python, Go, C, or C++. Strong SQL development and performance optimization skills, with familiarity with relational databases such as MySQL and PostgreSQL. Familiarity with one or more big data ecosystem technologies, such as Hadoop, Hive, Spark, Flink, Kafka, HBase, ClickHouse, or Elasticsearch. Experience in data warehouse development, data analysis, or machine learning is preferred. Strong engineering skills and problem-solving abilities, with the capability to independently analyze and resolve complex data-related challenges. A strong interest in AI technologies and data intelligence, along with a commitment to continuous learning.