[Contract] Analyst - Principal Analyst (Data Engineering Branch)

Singapore Public Service; Singapore; In loco; SGD 90.000 - 150.000; Tempo pieno

Descrizione del lavoro

Singapore Public Service through EMA seeks a data platform engineer to design, build and operate secure, scalable data pipelines and platforms across cloud and on‑premises environments. You will transform diverse energy-sector data into trusted data products to improve availability and time-to-insight.

The role requires hands-on data engineering, SQL proficiency, and experience with Python/Java/Scala, plus experience with AWS or Azure.

Competenze

• Degree in Computer Science, Information Systems, Engineering or a related technical discipline, or equivalent practical experience.
• Hands-on experience in data engineering, data integration, data platform development or a related field.
• Strong proficiency in SQL and at least one general-purpose programming language, preferably Python, Java or Scala.
• Sound understanding of ETL/ELT design, data modelling, relational and non-relational databases, data warehousing, data lakes or lakehouse architectures.
• Experience with data services on AWS or Microsoft Azure, such as object storage, serverless compute, orchestration, data integration, distributed processing and analytical databases.
• Knowledge of distributed data processing or streaming technologies, such as Apache Spark, PySpark or Kafka.
• Knowledge of data quality, metadata management, lineage, access control, privacy and data governance principles.
• Strong analytical and problem-solving skills, with the ability to diagnose complex data issues and develop pragmatic, sustainable solutions.
• Good written and verbal communication skills, with the ability to collaborate effectively across technical and business teams.
• A committed team player with a strong interest in public service and in shaping Singapore’s energy future.

Mansioni

• Design, build and operate robust batch and near-real-time data pipelines ingesting data from internal and external sources.
• Build and enhance cloud and on-premises data platforms, including data lakes, lakehouses, data warehouses and curated data layers.
• Develop ETL/ELT components, data models and transformation logic using SQL and languages like Python, Java or Scala.
• Implement automated data validation, lineage, monitoring and alerting to ensure accuracy and timeliness of data products.
• Optimise pipeline performance, storage, compute utilisation and query efficiency while managing service levels and costs.
• Apply software engineering practices including version control, code reviews, automated testing, CI/CD, documentation and configuration management.
• Monitor production workflows, troubleshoot failures, perform root-cause analysis and drive continuous improvements.
• Collaborate with business users, system owners and partners to translate data requirements into secure, scalable engineering solutions.
• Contribute to technical standards and reusable engineering patterns to improve EMA’s data engineering capabilities.

Conoscenze

SQL

Python

Java

Scala

ETL/ELT

Data modelling

Data warehousing

Data lakes

Spark

Kafka

AWS

Azure

Data governance

Problem solving

Communication

Formazione

Bachelor's degree or equivalent

Strumenti

AWS

Azure

Spark

Kafka

Descrizione del lavoro

What the role is

You will design, build and operate secure, scalable and reliable data platforms and pipelines that support EMA’s regulatory, operational, analytics and artificial intelligence needs. Working across cloud and on-premises environments, you will transform diverse energy-sector data into trusted, reusable data products that improve data availability, quality and time to insight.

What you will be working on

The key responsibilities of the officer include:

• Design, develop, test, deploy and maintain robust batch and near-real-time data pipelines that ingest and integrate structured, semi-structured and unstructured data from internal and external sources.
• Build and enhance cloud and on-premises data platforms, including data lakes, lakehouses, data warehouses and curated data layers, in accordance with enterprise architecture, security and governance standards.
• Develop reusable ETL/ELT components, data models and transformation logic using SQL and programming languages such as Python, Java or Scala.
• Implement automated data validation, reconciliation, monitoring, lineage and alerting controls to ensure the accuracy, completeness, freshness and reliability of data products.
• Optimise pipeline performance, storage, compute utilisation and query efficiency, while maintaining appropriate service levels and cost controls.
• Apply sound software engineering practices, including version control, code reviews, automated testing, CI/CD, technical documentation and configuration management.
• Monitor production data workflows, troubleshoot failures, perform root-cause analysis and implement sustainable improvements to platform reliability and maintainability.
• Work with business users, system owners, architects, analysts, data scientists, government agencies and industry partners to translate data requirements into secure, scalable and maintainable engineering solutions.
• Contribute to technical standards, reusable engineering patterns and continuous improvement of EMA’s data engineering capabilities.

What we are looking for

• Degree in Computer Science, Information Systems, Engineering or a related technical discipline, or equivalent practical experience.
• Hands-on experience in data engineering, data integration, data platform development or a related field. Fresh graduates with relevant internships, projects or technical portfolios may be considered for the entry-level position.
• Strong proficiency in SQL and at least one general-purpose programming language, preferably Python, Java or Scala.
• Sound understanding of ETL/ELT design, data modelling, relational and non-relational databases, data warehousing, data lakes or lakehouse architectures.
• Experience with data services on AWS or Microsoft Azure, such as object storage, serverless compute, orchestration, data integration, distributed processing and analytical databases, will be advantageous.
• Knowledge of distributed data processing or streaming technologies, such as Apache Spark, PySpark or Kafka, will be advantageous.
• Knowledge of data quality, metadata management, lineage, access control, privacy and data governance principles.
• Strong analytical and problem-solving skills, with the ability to diagnose complex data issues and develop pragmatic, sustainable solutions.
• Good written and verbal communication skills, with the ability to collaborate effectively across technical and business teams.
• A committed team player with a strong interest in public service and in shaping Singapore’s energy future.