Data Engineer (Databricks, PySpark, SQL)

Data Engineer (Databricks | PySpark | SQL) – 1-YearRenewable ContractSalary: Up to S$7,100/month + 1-Month Contract Completion BonusEmployment Type: 1-Year Renewable Contract
Experience Required: Minimum 3 years in Data Engineering or Enterprise Data Platforms

About the Role

Are you a hands-on Data Engineer with experience in Databricks and a passion for building reliable, scalable data pipelines?

We are looking for a Data Engineer to design, develop and support data ingestion and transformation pipelines using Databricks and AWS. You will work closely with architects, engineers, QA and business teams to deliver high-quality datasets and reliable data solutions for enterprise applications and analytics.

This is an opportunity to apply your technical skills to real-world data engineering challenges while working with modern cloud data technologies.

Key Responsibilities

• Design, develop and maintain batch and/or streaming data pipelines using Databricks and AWS services.
• Build data ingestion and transformation processes using SQL, Python/PySpark and Delta Lake.
• Process and organise data across Bronze, Silver and Gold layers using the Medallion Architecture.
• Integrate data from APIs, databases, files, object storage and other enterprise data sources.
• Implement data-quality checks, validation, reconciliation, logging, error handling and recovery mechanisms.
• Troubleshoot and optimise Spark jobs, data processing performance, storage and cloud resource utilisation.
• Support CI/CD, system integration testing (SIT), user acceptance testing (UAT), production deployment and operational handover.
• Prepare technical documentation and collaborate with platform, application, BI and data-governance teams.

Job Requirements

• Minimum 3 years of relevant experience in Data Engineering or enterprise data-platform delivery.
• Hands-on experience in Databricks Data Engineering is essential.
• Good working knowledge of PySpark, Apache Spark and SQL.
• Experience with Delta Lake and layered data architecture, such as Bronze, Silver and Gold.
• Experience developing and supporting production data pipelines, including logging, error handling and recovery.
• Familiarity with AWS services such as Amazon S3, IAM, AWS Glue, Lambda or Kinesis.
• Understanding of data modelling, data quality and performance optimisation.
• Strong analytical, troubleshooting and problem-solving skills.
• Able to work collaboratively, take ownership of assigned tasks and maintain proper technical documentation.

Good to Have