Data Engineer | Data Platform & Analytics

NEXBRIDGE RECRUITMENT PTE. LTD.; Singapore; On-site; SGD 70,000 - 110,000; Full time

Job summary

NEXBRIDGE RECRUITMENT PTE. LTD. is seeking a data engineer to design, develop and maintain ETL processes and data pipelines for ingestion, transformation and integration from multiple source agencies.

You will build modular, production-quality code and ensure outputs reconcile with production results. You will optimise legacy processes, design data schemas and storage structures, and develop reusable components, CI/CD pipelines and monitoring to support reliable data operations across

Qualifications

• 2 years of professional experience in data engineering, data platform development, software engineering or a related field.
• Strong proficiency in Python and SQL, with experience in complex data transformations, analysis and data quality monitoring.
• Hands-on experience with dataframe processing libraries such as Polars or pandas for processing large datasets efficiently.
• Experience with database management, data warehousing and ETL development for data ingestion and integration.
• Experience with Git and GitLab, including branching strategies, merge requests, code reviews and CI/CD pipelines.
• Working knowledge of Linux/Unix environments, shell scripting and command-line tools.
• Understanding of data governance, access control and security practices.
• Knowledge of software engineering practices including modular design, configuration management, logging, exception handling, testing and technical documentation.
• Experience with cloud technologies, workflow orchestration or CI/CD tools will be an advantage.
• Strong problem-solving and analytical skills with good communication and stakeholder management abilities.
• Ability to work independently and collaboratively within a technical team.

Responsibilities

• Design, develop and maintain efficient and reliable ETL processes and data pipelines for data ingestion, transformation and integration from multiple source agencies.
• Develop and maintain modular, production-quality code for statistical production processes while preserving existing business logic and ensuring outputs are accurately reconciled against current production results.
• Support the migration, modernisation and optimisation of legacy processes to improve efficiency, maintainability and reliability.
• Design and maintain database schemas, data models and storage structures to support the efficient ingestion, validation, transformation and retrieval of large datasets.
• Develop reusable data engineering components, including common libraries, configuration frameworks, logging standards and utilities to streamline production workflows.
• Implement and maintain CI/CD pipelines for data engineering workflows, including automated testing and deployment.
• Manage workflows across Development, UAT and Production environments to support safe testing, deployment and synchronisation.
• Design and configure complex data pipeline workflows and schedules, including dependencies, failure handling, recovery and operational monitoring.
• Establish and maintain version control structures using GitLab, including branching strategies, merge requests, code reviews and technical documentation.
• Monitor and troubleshoot data pipelines, conduct root-cause analysis and continuously improve system reliability and performance.
• Collaborate with technical teams, business users and stakeholders to translate business and statistical processing requirements into robust technical solutions.

Skills

Python

SQL

Polars

Pandas

ETL

GitLab CI/CD

Linux/Unix

Data governance

Cloud

Data pipelines

Tools

GitLab

CI/CD

Job description

Job Responsibilities

• Design, develop and maintain efficient and reliable ETL processes and data pipelines for data ingestion, transformation and integration from multiple source agencies.
• Develop and maintain modular, production-quality code for statistical production processes while preserving existing business logic and ensuring outputs are accurately reconciled against current production results.
• Support the migration, modernisation and optimisation of legacy processes to improve efficiency, maintainability and reliability.
• Design and maintain database schemas, data models and storage structures to support the efficient ingestion, validation, transformation and retrieval of large datasets.
• Develop reusable data engineering components, including common libraries, configuration frameworks, logging standards and utilities to streamline production workflows.
• Implement and maintain CI/CD pipelines for data engineering workflows, including automated testing and deployment.
• Manage workflows across Development, UAT and Production environments to support safe testing, deployment and synchronisation.
• Design and configure complex data pipeline workflows and schedules, including dependencies, failure handling, recovery and operational monitoring.
• Establish and maintain version control structures using GitLab, including branching strategies, merge requests, code reviews and technical documentation.
• Monitor and troubleshoot data pipelines, conduct root-cause analysis and continuously improve system reliability and performance.
• Collaborate with technical teams, business users and stakeholders to translate business and statistical processing requirements into robust technical solutions.

Job Requirements

• 2 years of professional experience in data engineering, data platform development, software engineering or a related field.
• Strong proficiency in Python and SQL, with experience in complex data transformations, analysis and data quality monitoring.
• Hands-on experience with dataframe processing libraries such as Polars or pandas for processing large datasets efficiently.
• Experience with database management, data warehousing and ETL development for data ingestion and integration.
• Experience with Git and GitLab, including branching strategies, merge requests, code reviews and CI/CD pipelines.
• Working knowledge of Linux/Unix environments, shell scripting and command-line tools.
• Understanding of data governance, access control and security practices.
• Knowledge of software engineering practices including modular design, configuration management, logging, exception handling, testing and technical documentation.
• Experience with cloud technologies, workflow orchestration or CI/CD tools will be an advantage.
• Strong problem-solving and analytical skills with good communication and stakeholder management abilities.
• Ability to work independently and collaboratively within a technical team.

We regret that only shortlisted candidates will be notified.

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