Senior Data Engineer

Argyll Scott Singapore; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Argyll Scott Singapore is seeking a Senior Data Engineer to design scalable data platforms and modern data pipelines that power analytics, ML, and real-time customer experiences. You will work across product, analytics, and engineering to build reliable data solutions in the cloud.

Join a fast-growing consumer technology business and partner with ML engineers to enable model training, real-time inference, and personalised recommendations while advancing CI/CD, testing, data quality, and platform

Qualifications

• 4+ years of data engineering experience with SQL, Python, and data modelling.
• Hands-on with BigQuery, dbt, Airflow and cloud platforms (GCP/AWS/Azure).
• Experience supporting ML data pipelines or real-time personalization.
• Strong communication and stakeholder management skills.

Responsibilities

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

Skills

SQL

Python

Data modelling

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.

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.

Requirements

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).