Data Engineer — AMK

COMBUILDER PTE LTD; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

COMBUILDER PTE LTD is seeking a data engineer to design, build, and maintain scalable data pipelines. You will work across batch and streaming workloads, collaborate with application teams, and ensure data quality and governance.

Responsibilities include migrating legacy workflows, implementing ETL, and optimizing performance on cloud-based platforms using Spark and SQL Server. The role emphasizes reliability, logging, and CI/CD for data workflows.

Qualifications

• 3–5 years of experience in Data Engineering or a related role.
• Experience with workflow orchestration or integration platforms.
• Hands-on experience with ETL and file-based data ingestion.
• Proficient in SQL and relational databases such as SQL Server.
• Experience with Spark or other distributed data processing frameworks.
• Experience with Python or other programming languages.
• Understanding of batch and streaming data processing.
• Experience with cloud platforms and managed data services.
• Experience working with REST APIs, JSON and CSV.
• Understanding of CI/CD for data and integration workflows.
• Good understanding of data quality, error handling and pipeline reliability.

Responsibilities

• Analyse and migrate existing workflow integrations to new platforms.
• Develop workflow orchestration, including triggers, routing, retries and exception handling.
• Build automation for batching, pagination, looping and throttling.
• Design and maintain ETL pipelines for file-based data ingestion into relational databases.
• Perform schema validation, data quality checks and error reconciliation.
• Develop data pipelines using cloud-based big data platforms and distributed processing frameworks such as Spark.
• Support Lakehouse-based data environments for batch and streaming workloads.
• Implement logging, monitoring and alerting for data pipelines and workflows.
• Troubleshoot issues and participate in root-cause analysis and continuous improvement.
• Work with application teams to understand data requirements and upstream/downstream dependencies.
• Ensure data pipelines meet performance, reliability, security and governance requirements.

Skills

Data engineering

Workflow orchestration

Python

SQL

Batch & streaming processing

CI/CD for data

Tools

Spark

SQL Server

REST APIs

JSON

CSV

Job description

Job Responsibilities

• Analyse and migrate existing workflow integrations to new platforms.
• Develop workflow orchestration, including triggers, routing, retries and exception handling.
• Build automation for batching, pagination, looping and throttling.
• Design and maintain ETL pipelines for file-based data ingestion into relational databases.
• Perform schema validation, data quality checks and error reconciliation.
• Develop data pipelines using cloud-based big data platforms and distributed processing frameworks such as Spark.
• Support Lakehouse-based data environments for batch and streaming workloads.
• Implement logging, monitoring and alerting for data pipelines and workflows.
• Troubleshoot issues and participate in root-cause analysis and continuous improvement.
• Work with application teams to understand data requirements and upstream/downstream dependencies.
• Ensure data pipelines meet performance, reliability, security and governance requirements.

Job Requirements

• 3–5 years of relevant experience in Data Engineering, Integration Engineering or a related role.
• Experience with workflow orchestration or integration platforms.
• Hands-on experience with ETL and file-based data ingestion.
• Proficient in SQL and relational databases such as SQL Server.
• Experience with Spark or other distributed data processing frameworks.
• Experience with Python or other programming/scripting languages.
• Understanding of batch and streaming data processing.
• Experience with cloud platforms and managed data services.
• Experience working with REST APIs, JSON and CSV.
• Understanding of CI/CD for data and integration workflows.
• Good understanding of data quality, error handling and pipeline reliability.

Good to Have

• Experience with Lakehouse table formats, incremental processing and time travel.
• Experience with Flink or Spark Streaming.
• Familiarity with analytical query engines.
• Experience in banking, financial services, manufacturing or other regulated environments.
• Knowledge of data observability and data quality tools.