Senior Data Engineer

Kerry Consulting; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Kerry Consulting in Singapore is seeking a Senior Data Engineer to design and build scalable data platforms and lead delivery of high-quality data engineering solutions.

You will stay hands-on while providing technical leadership, shaping engineering standards, and collaborating with analytics, software, and AI/ML teams to enable analytics and Generative AI workloads.

Qualifications

• 5-10 years of experience designing and delivering production-grade data platforms and pipelines.
• Hands-on experience with cloud-based data platforms and architectures (AWS, Azure, or GCP).
• Strong Python and SQL skills; experience with distributed processing (Spark/PySpark).
• Experience with data orchestration, data quality, and observability practices.

Responsibilities

• Lead design, development, and evolution of scalable data platforms and pipelines for analytics and AI workloads.
• Provide technical leadership across data architecture, modelling, ETL/ELT, distributed processing, orchestration, data quality, and platform reliability.
• Champion AI-enabled engineering practices using copilots, agents, and AI tools to improve development productivity and quality.
• Mentor engineers, conduct design and code reviews, and grow engineering capability within the team.

Skills

Python

SQL

Spark

Data modelling

ETL/ELT

Tools

AWS

Azure

GCP

Job description

This is a fast-growing organization investing in modern data and AI capabilities to support digital products, analytics, and AI-driven innovation. As part of its continued expansion, the organization is seeking to appoint a Senior Data Engineer to design and build scalable data platforms and lead the delivery of high-quality data engineering solutions.

This is an excellent opportunity for an experienced data engineer who wants to remain hands-on while providing technical leadership, shaping engineering standards, and working closely with software, analytics, and AI/ML teams.

Responsibilities

About the job

This is a fast-growing organization investing in modern data and AI capabilities to support digital products, analytics, and AI-driven innovation. As part of its continued expansion, the organization is seeking to appoint a Senior Data Engineer to design and build scalable data platforms and lead the delivery of high-quality data engineering solutions.

This is an excellent opportunity for an experienced data engineer who wants to remain hands-on while providing technical leadership, shaping engineering standards, and working closely with software, analytics, and AI/ML teams.

Responsibilities

You will lead the design, development, and evolution of scalable data platforms and pipelines supporting analytics, digital products, and AI/ML workloads. You will provide technical leadership across data architecture, data modelling, ETL/ELT, distributed processing, orchestration, data quality, and platform reliability. Working closely with data engineers, software engineers, data scientists, and AI engineers, you will design robust data pipelines and ensure that data is reliable, accessible, and optimized for downstream consumption. You will establish engineering standards around testing, validation, observability, CI/CD, and automation while guiding the team on system design and architectural decisions. You will also champion the adoption of AI-enabled engineering practices, leveraging copilots, agents, and other AI development tools to accelerate development, improve code and data quality, automate engineering workflows, and enhance overall team productivity. As a technical lead, you will mentor engineers, conduct design and code reviews, and help develop engineering capability across the team.

Requirements

We are looking for a Senior Data Engineer with 5-10 years of experience designing and delivering production-grade data platforms and pipelines, with demonstrated experience providing technical leadership to engineering teams. You should have strong experience with modern cloud-based data platforms and architectures, with hands-on expertise building scalable data solutions in AWS, Azure, or Google Cloud environments. Strong proficiency in Python and SQL is essential, together with practical experience in distributed data processing technologies such as Apache Spark / PySpark. You should have hands-on experience with data orchestration and workflow management, as well as establishing data quality practices covering automated testing, validation, monitoring, and observability. A solid understanding of data modelling, data warehousing, and modern data architecture principles is essential. Experience building and operating data pipelines that support analytics, machine learning, and Generative AI workloads is highly desirable. You should also have practical exposure to AI-enabled data engineering practices, including using copilots, AI agents, or similar tools across the engineering lifecycle to improve development productivity, automation, testing, documentation, and solution quality. The ideal candidate will combine strong hands-on engineering capability with proven experience in system design, architecture, technical decision-making, and mentoring or leading data engineers.