Data Engineer Lead

About the role

DT One is seeking a highly driven, self-motivated Data Engineer Lead based in Singapore or Manila. You will design, develop and maintain a data and AI platform that works at scale, working with Engineering Leads, Engineers, Data Scientists, Business Analysts and other teams to deliver a high-performing system that addresses daily operational challenges and supports organisational scalability.

Key responsibilities

• Design and build a highly reliable data lake operating at scale, with a strong emphasis on data quality, integrity and availability.
• Build complex new data pipelines to continuously expand capabilities with new data sources.
• Develop tools, internal libraries, automated ETL creation and data exploration frameworks for Data Scientists, Business Analysts and Engineers.
• Migrate Machine Learning and Data Applications from POC/MVP stage to production grade, ensuring high availability, scalability and security.
• Architect and scale infrastructure for Generative AI, LLM deployment, model fine-tuning and vector retrieval (RAG).
• Monitor and maintain all data systems, including ETLs, Data Warehouses and Data Applications.
• Provide actionable insights on Data Governance, industry best practices and large-scale processing.
• Support cross-functional teams with guidance on data usage, processing and platform adoption.

Required profile

• Expertise in medium- to large-scale data processing systems, preferably on open-source systems or AWS services.
• Strong software development skills in one or more languages such as Python, Java or Scala.
• Advanced knowledge of SQL and SQL database administration.
• Hands-on experience with AWS infrastructure and Docker frameworks.
• Solid background in Data Warehousing and Data Modeling.
• Bias towards action and shipping, with a focus on data compliance, security, quality and integrity.

Required skills

• Distributed processing systems such as Spark, Presto or Hadoop.
• MLOps frameworks, LLM orchestration tools (e.g. LangChain, LlamaIndex) and vector databases (e.g. Pinecone, Milvus, Qdrant).