Data Engineer

JOBSTER PRIVATE LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

JOBSTER PRIVATE LTD. Singapore is seeking a data engineer to design and operate production-grade ETL/ELT pipelines for ingestion, transformation and loading.

You will integrate data from APIs, databases, SaaS and cloud services, build data models and ensure data quality and governance across hybrid environments.

Candidates should have 3–5 years of experience and hands-on practice with AWS/Azure data tools, CI/CD, and IaC.

Qualifications

• 3–5 years of experience in data engineering, cloud data engineering, analytics engineering, software engineering, or a related discipline.
• Hands-on experience designing, building, and operating production-grade data pipelines.
• Experience with data extraction, ingestion, ETL/ELT, transformation, data modelling, and data quality.
• Experience using AWS and/or Azure native data capabilities.

Responsibilities

• Design and operate production-grade ETL/ELT data pipelines for data ingestion, transformation and loading.
• Integrate data from APIs, databases, enterprise systems, SaaS, files, streams and cloud services.
• Develop batch, incremental, CDC and event-driven data pipelines.
• Design data models, schemas, data lakes and analytical datasets.
• Implement data quality, validation, monitoring, reconciliation and reliability controls.
• Build trusted datasets for analytics, reporting, operational visibility and AI/ML use cases.
• Design secure data integration across on-premise, AWS and hybrid environments.
• Ensure data security, access control, governance, lineage and auditability.
• Monitor production pipelines, troubleshoot incidents and improve performance, reliability and cost.
• Apply software engineering practices including Git, automated testing, CI/CD and Infrastructure as Code.

Skills

Data engineering

Cloud data

ETL/ELT design

Data modeling

Data quality

API data integration

CI/CD

IaC

Git

Testing

Tools

AWS

Azure

Job description

Responsibilities:

• Design and operate production-grade ETL/ELT data pipelines for data ingestion, transformation and loading.
• Integrate data from APIs, databases, enterprise systems, SaaS, files, streams and cloud services.
• Develop batch, incremental, CDC and event-driven data pipelines.
• Design data models, schemas, data lakes and analytical datasets.
• Implement data quality, validation, monitoring, reconciliation and reliability controls.
• Build trusted datasets for analytics, reporting, operational visibility and AI/ML use cases.
• Design secure data integration across on-premise, AWS, Azure and hybrid environments.
• Ensure appropriate data security, access control, governance, lineage and auditability.
• Monitor production pipelines, troubleshoot incidents and improve performance, reliability and cost.
• Apply software engineering practices including Git, automated testing, CI/CD and Infrastructure as Code.
  

Requirments:

• Minimum 3–5 years of experience in data engineering, cloud data engineering, analytics engineering, software engineering, or a related discipline
• At least 2 years of hands-on experience designing, building, and operating production-grade data pipelines
• Demonstrated experience with data extraction, ingestion, ETL/ELT, transformation, data modelling, and data quality
• Experience using AWS and/or Azure native data capabilities
• Experience integrating data from APIs, databases, enterprise systems, files, or streaming sources
• Experience implementing batch, incremental, CDC, and/or event-driven data pipelines
• Experience working with on-premises and/or cloud environments, with an understanding of hybrid integration patterns
• Experience applying software-engineering practices such as version control, automated testing, CI/CD, monitoring, and Infrastructure as Code to data solutions