Senior Data Engineer
Summary
Design and optimize AI data pipelines, feature stores, and real-time inference systems using Python, Spark, and cloud infrastructure to power machine learning models.
Senior Data Engineer
Job Responsibilities
Lead the technical initiatives for AI data engineering, enabling scalable, high-performance data pipelines that power AI and machine learning applications. Design, optimize, and manage data infrastructure to support AI model training, feature engineering, and real-time inference. Collaborate closely with AI/ML engineers, data scientists, and platform teams to build the next generation of AI-driven products. Drive the design and development of robust data pipelines for AI/ML workloads, ensuring efficiency, scalability, and reliability. Design and implement data architectures that support AI model training, including feature stores, vector databases, and real-time streaming solutions. Develop high-performance pipelines that process structured, semi-structured, and unstructured data at scale. Implement best practices for data quality, lineage, security, and compliance. Develop automated data workflows and integrate with DevOps and MLOps frameworks to enable model reproducibility. Continuously explore advancements in AI data engineering, such as distributed computing, data mesh architectures, and next-generation storage solutions. Work closely with data scientists and AI/ML engineers to optimize feature extraction, data labeling, and real-time inference pipelines.
Pre-Requisites
Hands-on experience working with Vector/Graph or Neo4j. Minimum three years of experience in data engineering, working on AI/ML-driven data architectures. Ability to work in fast-paced, high-pressure, agile environments. Strong programming skills in Python and SQL.
Experience developing and deploying applications on cloud infrastructure (AWS, Azure, or Google Cloud Platform) using Infrastructure as Code tools such as Terraform, containerization tools such as Docker, and container orchestration platforms such as Kubernetes. Knowledge of orchestration tools (Airflow or Prefect), distributed computing frameworks (Spark or Dask), and data transformation tools (Data Build Tool - DBT). Expertise in both streaming and batch data processing, and managing and optimizing data storage (Data Lake, Lake House, SQL and NoSQL databases). Experience with network infrastructure and real-time AI inference pipelines using event-driven architectures.
Excellent problem-solving, analytical skills, and understanding of generative AI technologies and their applications. Strong written and verbal communication skills for coordinating across teams. A passion for staying current with advances in data engineering.
Education & Experience
Bachelor’s or Master’s degree in computer science, data engineering, AI/ML, or a related field from an accredited institution.
Travel Requirements
Role based in the Singapore office; may require up to one travel trip per year.
Equal Opportunity Employer
We are an equal‑opportunity employer. We do not discriminate on the basis of race, ethnicity, colour, nationality, ancestry, religion, age, sex, sexual orientation, gender identity or expression, disability, marital status, or any other characteristic protected by the laws of the countries where we operate. We also provide reasonable accommodations where needed, including for disability or religious practices, so every team member can perform and contribute at their best.