Staff Data Engineer

The Knowledge Platform team is at the heart of our company's data and AI strategy. We are building the foundational infrastructure that empowers the entire company to leverage data, AI, and ML into business impact. You'll design and build scalable data pipelines and data models that power critical use cases across Knowledge Platform teams and business domains. You'll partner closely with engineering and cross-functional stakeholders to turn ambiguous data needs into reliable, well-structured solutions that support decision-making and product development. This role is based in Singapore.

Responsibilities

Design and build scalable data pipelines and data models that power critical use cases across Knowledge Platform teams and business domains; Partner closely with engineering and cross-functional stakeholders to turn ambiguous data needs into reliable, well-structured solutions that support decision-making and product development; Own projects end to end, from system and pipeline design to performance, monitoring, and ongoing improvements in data quality and reliability; Help raise the bar on technical design and execution by bringing strong engineering judgment, practical problem-solving, and a high standard of ownership; Design and implement robust and scalable data models that support business intelligence, machine learning, and operational needs; Possess a deep understanding of data schemas and be able to select appropriate schema designs (e.g., star schema, snowflake, normalized vs denormalized) based on use cases; Collaborate closely with business teams to translate their data needs into clean, structured, and well-documented models; Understand and promote the concept of SSOT (Single Source of Truth) throughout the data layers and pipelines; Maintain data consistency, traceability, and quality across multiple data sources and domains; Build and maintain both batch and streaming ETL pipelines, with a strong understanding of end-to-end data workflow from data ingestion to transformation and delivery; Work closely with Data Platform Engineers (DPEs) and Product Managers (PMs) to quickly identify root causes of data issues and provide efficient, scalable solutions; Participate in and contribute to data governance strategies, policies, and standards; Be familiar with any of the six key pillars of traditional data governance (e.g., data quality, data stewardship, metadata management, master data management, data privacy/security, data lifecycle); Understand AI and enjoy thinking about how data engineering and AI can work together in practical and creative ways

Requirements

Bachelor's degree or higher in Computer Science, Information Systems, Finance, Mathematics, or a related field; Minimum 7 years of proven experience designing and implementing ETL pipelines, with a strong understanding of the strategies, rules, and processes involved in building scalable and reliable data pipelines; Experience with streaming data pipelines or real-time data processing technologies, such as Apache Kafka or Apache Flink; Proficiency in SQL, database management systems (e.g., MySQL, PostgreSQL, Oracle), and data warehousing solutions; Familiarity with Google Cloud Platform (GCP) and modern data infrastructure tools, including Databricks and Airflow; Knowledge of data governance practices and regulatory requirements within the financial industry; Excellent problem-solving skills with strong attention to detail and a commitment to producing high-quality work; Strong communication and collaboration skills, with the ability to work effectively in a fast-paced, team-oriented environment; Outstanding verbal communication skills, with the ability to collaborate effectively with globally distributed teams

Nice to have

Experience with financial industries, payment systems, or fintech platforms; Knowledge of data governance practices and regulatory requirements in the financial industry; Experience with scripting languages (e.g., Python, R) for data analysis and automation; Certification in data management or related technologies; Bonus if you've worked with data across distributed or multi-datacenter systems, including solving challenges related to data migration, duplication, and consistency