Data Engineer

FPT Asia Pacific; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A leading technology firm in Singapore is seeking a skilled Data Engineer to design and maintain robust end-to-end data pipelines for machine learning and AI applications. This hands-on role requires extensive experience in building and optimizing data systems in cloud environments, specifically AWS. The ideal candidate will hold a Bachelor’s or Master’s degree in a relevant technical field and possess 3-5 years of experience in data engineering. Strong proficiency in Python and SQL, along with knowledge of cloud data platforms and big data ecosystems, is essential for success in this position.

Qualifications

• 3-5 years of experience in data engineering roles.
• Proven ability to design and deliver scalable, production-grade data systems.

Responsibilities

• Design, develop, and maintain end-to-end data pipelines for ML and AI workloads.
• Build real-time and batch data processing systems.
• Implement data quality, validation, and transformation workflows.
• Develop and operate cloud-based data architectures on AWS and hybrid environments.
• Containerize and orchestrate workloads using Docker and Kubernetes.

Skills

Python

SQL

Shell scripting

Infrastructure as Code (Terraform, CloudFormation, CDK)

Cloud data platforms (AWS, GCP)

Big data ecosystems (e.g., Spark, Hadoop)

Streaming technologies (Kafka, Kinesis)

Containerization (Docker)

Orchestration (Kubernetes)

Data engineering

Education

Bachelors or Masters in Computer Science, Engineering, or related technical field

Job description

You will work closely with ML engineers, data scientists, and cloud architects to ensure that data is reliable, scalable, and production-ready enabling rapid experimentation and deployment of AI models across airport systems.

This is a hands-on, delivery-focused role suited for engineers who enjoy building robust data systems in a fast-moving environment.

Key Responsibilities

• Design, develop, and maintain end-to-end data pipelines to support ML and AI workloads.
• Build real-time and batch data processing systems using Kafka, Kinesis, Spark, or similar technologies.
• Implement data quality, validation, and transformation workflows to ensure trustworthy and high-quality data for model training and analytics.
• Develop and operate cloud-based data architectures on AWS and hybrid on-prem environments.
• Build and manage data warehouses and data lakes for efficient storage, retrieval, and sharing.
• Use Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, or CDK to automate data environment provisioning.
• Containerize and orchestrate workloads using Docker and Kubernetes.

MLOps & Integration

• Partner with ML engineers to streamline feature pipelines, model training, and inference data flows.
• Support retrieval-augmented generation (RAG) and GenAI agent systems through optimized data access and embeddings infrastructure.
• Ensure seamless integration of data systems into CI/CD pipelines for continuous delivery and monitoring.

Security & Operations

• Implement and uphold data, access control, and privacy compliance standards.
• Monitor and optimize pipelines for cost efficiency, latency, and reliability.
• Collaborate with DevOps and IT teams to troubleshoot, deploy, and scale data services in production environments.
• Work closely with cross-functional teams to translate business and research data needs into scalable technical solutions.
• Evaluate emerging data engineering technologies and architectures relevant to AI workloads.
• Contribute to internal documentation and knowledge sharing to support continuous improvement.

Qualifications

• Bachelors or Masters in Computer Science, Engineering, or related technical field.
• 3 -5 years of experience in data engineering roles.

Proficiency in

• Python and SQL
• Shell scripting
• Infrastructure as Code (Terraform, CloudFormation, CDK)
• Strong experience with cloud data platforms (AWS, GCP) and hybrid/on-prem environments.
• Solid understanding of big data ecosystems (e.g., Spark, Hadoop) and streaming technologies (Kafka, Kinesis).
• Familiarity with containerization (Docker) and orchestration (Kubernetes).
• Proven ability to design and deliver scalable, production-grade data systems.

Bonus Skills

• Exposure to MLOps, feature stores, or vector databases (e.g., FAISS, Pinecone, Weaviate).
• Interest in data observability, metadata management, and responsible data practices.