Data Engineer (Databricks | PySpark | SQL)
1-year renewable contract Data Engineer role (via ES Recruitment) building and maintaining batch/streaming data pipelines with Databricks, PySpark, SQL and Delta Lake on AWS, using Medallion Architecture, plus data-quality checks and CI/CD support. Pays up to S$7,100/month plus a 1-month completion bonus.
- Compensation
- SGD 6.4K–SGD 7.1K / mo
- Location
- Ang Mo Kio, North-East Region
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- Minimum 3 years of relevant experience in Data Engineering or enterprise data-platform delivery
- Hands-on experience in Databricks Data Engineering (essential)
- Good working knowledge of PySpark, Apache Spark and SQL
- Experience with Delta Lake and layered data architecture (Bronze, Silver, Gold)
- Experience developing and supporting production data pipelines, including logging, error handling and recovery
- Familiarity with AWS services such as 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 tasks and maintain technical documentation
Nice to have
- Experience with Databricks Workflows, Delta Live Tables, Unity Catalog, Auto Loader or Structured Streaming
- Exposure to API integration, JSON/XML data formats or near-real-time data ingestion
- Familiarity with Git, CI/CD and infrastructure or configuration automation
- Experience with BI datasets, Tableau or data warehouse integration
- Relevant AWS, Databricks or Data Engineering certifications
Full description
Salary: $6,400 – $7,100 per month
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.