Senior Data Engineer

Argyll Scott; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Argyll Scott is seeking a Senior Data Engineer in Singapore to design scalable data platforms and reliable pipelines for analytics and ML initiatives. You will collaborate with product and analytics teams to deliver real-time personalized customer experiences.

The role requires hands-on experience with SQL, Python, data modelling, and cloud data stacks such as BigQuery, dbt, Airflow, across GCP/AWS/Azure. Strong communication and problem-solving are essential.

Qualifications

• 4+ years of experience in Data Engineering.
• Strong SQL, Python and data modelling expertise.
• Hands-on experience with cloud data stacks and data pipelines.
• Experience with ML data pipelines or real-time personalization data.

Responsibilities

• Design and maintain scalable cloud-based data pipelines and modern data warehouse solutions.
• Build robust data models and high-quality datasets to support analytics and products.
• Partner with ML engineers to enable model training, real-time inference, and recommendations.
• Drive CI/CD, testing, monitoring, data quality, and platform reliability.

Skills

SQL

Python

Data modelling

Data pipelines

Cloud data stacks

Tools

BigQuery

dbt

Airflow

GCP

AWS

Azure

Job description

Senior Data Engineer

Our client is a fast-growing consumer technology business, investing in their next-generation data platform to enable advanced analytics, machine learning, and real-time personalised customer experiences.

They are on the lookout for an experienced Senior Data Engineer to design scalable data platforms, build reliable pipelines, and support analytics and machine learning initiatives across the business.

Our client is a fast-growing consumer technology business, investing in their next-generation data platform to enable advanced analytics, machine learning, and real-time personalised customer experiences.

They are on the lookout for an experienced Senior Data Engineer to design scalable data platforms, build reliable pipelines, and support analytics and machine learning initiatives across the business.

What You’ll Do

• Design and maintain scalable cloud-based data pipelines and modern data warehouse solutions.
• Build robust data models and high-quality datasets to support analytics, product, and business teams.
• Partner with Machine Learning Engineers to enable model training, real-time inference, and recommendation systems.
• Drive engineering best practices across CI/CD, testing, monitoring, data quality, and platform reliability.

Essential Skills & Experience

• 4+ years of experience in Data Engineering with strong SQL, Python, and data modelling expertise.
• Hands-on experience with modern cloud data stacks such as BigQuery, dbt, Airflow, and cloud platforms (GCP, AWS, or Azure).
• Experience supporting machine learning data pipelines, recommendation engines, or real-time personalisation platforms.
• Strong communication, problem-solving, and stakeholder management skills, with the ability to work collaboratively across engineering, product, and analytics teams.

Nice to Have

• Experience with streaming or distributed data processing technologies such as Spark, Kafka, Beam, or Flink.
• Exposure to MLOps, feature stores, or production ML infrastructure.
• Experience with serverless or containerised cloud environments (e.g. Cloud Run, ECS, Kubernetes).