Junior Data Engineer

Kerry Consulting; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

Kerry Consulting is recruiting a Junior Data Engineer for a fast-growing technology partner in Singapore. The role focuses on building scalable data pipelines and modern data infrastructure to power AI and analytics initiatives.

You will work with data engineers, software engineers, and data scientists to design, develop, and maintain ETL/ELT pipelines, write SQL and PySpark jobs, and ensure data quality and reliability across enterprise data platforms and cloud environments.

Qualifications

• 1‑3 years of experience in data engineering, ETL development or related field.
• Strong SQL skills and experience with ETL/ELT pipelines.
• Hands-on PySpark experience for large-scale data processing.
• Experience with Python, relational databases, and modern data platforms such as Databricks, Snowflake, Azure Data Factory, Apache Airflow, AWS Glue, or Apache Spark is highly desirable.
• Familiarity with cloud environments (AWS, Azure, or Google Cloud) and data orchestration tools will be advantageous.
• Candidates should possess strong analytical and problem-solving skills and the ability to collaborate with teams to build reliable data solutions.

Responsibilities

• Design, develop, and maintain ETL/ELT data pipelines that ingest, transform, and deliver data from multiple sources into enterprise data platforms.
• Working closely with data engineers, data scientists, and application development teams to ensure high-quality, reliable, and scalable data pipelines for analytics, reporting, and AI initiatives.
• Write and optimize SQL queries, develop and optimize PySpark data processing jobs, perform data validation and quality checks, troubleshoot pipeline issues, and contribute to improving data reliability and performance.
• Assist with data modelling, documentation, pipeline monitoring, and automation while adopting modern data engineering practices and cloud technologies.

Skills

SQL

PySpark

Python

Data modelling

Cloud platforms

Databricks

Snowflake

Azure Data Factory

Apache Airflow

AWS Glue

Apache Spark

Tools

Databricks

Snowflake

Azure Data Factory

Apache Airflow

AWS Glue

Apache Spark

Job description

About the job

This is a fast-growing technology company that is building scalable data platforms to power AI, analytics, and next-generation digital products. As part of its continued expansion, the organization is seeking to appoint a Junior Data Engineer to support the development of reliable data pipelines and modern data infrastructure.

This is an excellent opportunity for an early-career data professional to work alongside experienced data engineers, software engineers, and data scientists, gaining hands‑on experience building enterprise‑scale data platforms and enabling AI‑driven products.

Responsibilities

You will design, develop, and maintain ETL/ELT data pipelines that ingest, transform, and deliver data from multiple sources into enterprise data platforms. Working closely with data engineers, data scientists, and application development teams, you will ensure high-quality, reliable, and scalable data pipelines that support analytics, reporting, and AI initiatives.

You will write and optimize SQL queries, develop and optimize PySpark data processing jobs, perform data validation and quality checks, troubleshoot pipeline issues, and contribute to improving data reliability and performance. You will also assist with data modelling, documentation, pipeline monitoring, and automation while adopting modern data engineering practices and cloud technologies.

Requirements

We are looking for a Junior Data Engineer with 1-3 years of experience in data engineering, database development, or ETL development within a technology or data-driven environment.

You should have strong SQL skills and hands‑on experience designing and maintaining ETL/ELT pipelines. Hands‑on experience with PySpark is required, including developing and optimizing distributed data processing pipelines and ETL workflows for large‑scale datasets.

Experience with Python, relational databases, and modern data platforms such as Databricks, Snowflake, Azure Data Factory, Apache Airflow, AWS Glue, or Apache Spark is highly desirable. Familiarity with cloud environments (AWS, Azure, or Google Cloud), data modelling, and data orchestration tools will be advantageous.

Candidates should possess strong analytical and problem‑solving skills, attention to data quality, and the ability to work collaboratively with data engineers, software engineers, and data scientists to build reliable, scalable data solutions.