We are seeking a Data Engineering & Analytics Engineer to design, build, and support enterprise-grade data platforms and pipelines. This role is responsible for the end-to-end data lifecycle, including ingestion, transformation, modelling, quality management, governance, and delivery of trusted datasets for applications, reporting, analytics, and AI/ML workloads.
The successful candidate will work closely with software engineers, platform engineers, and business stakeholders to deliver scalable, secure, and reliable data solutions across on-premises and cloud environments.
Key Responsibilities
Data Engineering & Integration
- Design, build, and maintain production-grade ETL/ELT data pipelines.
- Integrate data from enterprise applications, APIs, databases, SaaS platforms, cloud services, files, and streaming sources.
- Develop batch, incremental, change data capture (CDC), event-driven, and streaming data ingestion solutions.
- Implement data transformation processes to cleanse, enrich, standardise, and consolidate data into trusted datasets.
- Design secure and resilient data integration solutions across on-premises, GCC, AWS, Azure, and other approved environments.
- Automate deployment, testing, monitoring, and operational processes for data platforms.
Data Architecture & Analytics
- Develop and maintain data lakes, analytical datasets, and cloud-native data platforms.
- Design logical and physical data models to support operational reporting, analytics, and machine learning initiatives.
- Create reusable data products and datasets that support business intelligence, operational visibility, and decision-making.
- Collaborate with stakeholders to translate business requirements into robust data solutions.
Data Quality, Governance & Security
- Implement data validation, reconciliation, monitoring, and quality controls throughout the data lifecycle.
- Monitor pipeline health, data freshness, completeness, and reliability.
- Maintain data lineage, metadata, auditability, and traceability.
- Ensure compliance with security policies, access controls, data classification, retention, and governance requirements.
Operations & Continuous Improvement
- Support production data platforms and pipelines.
- Investigate incidents, perform root cause analysis, and implement preventive measures.
- Monitor platform performance, reliability, scalability, and costs.
- Maintain technical documentation, runbooks, and operational procedures.
- Drive continuous improvements in automation, reliability, and operational efficiency.
Requirements
Experience
- Degree in Computer Science, Information Technology, Engineering, Data Analytics, or a related discipline.
- Minimum 3 to 5 years of experience in Data Engineering, Analytics Engineering, Cloud Data Engineering, Software Engineering, or related fields.
- At least 2 years of hands-on experience designing, building, and supporting production-grade data pipelines.
- Experience with data extraction, ingestion, ETL/ELT, data transformation, modelling, and quality management.
- Experience integrating data from APIs, databases, enterprise systems, files, and streaming platforms.
- Experience with AWS and/or Microsoft Azure cloud services.
- Familiarity with hybrid cloud and on-premises integration architectures.
- Experience applying software engineering best practices including version control, CI/CD, automated testing, monitoring, and Infrastructure as Code.
Technical Skills
- Python and SQL development.
- Data engineering concepts including ETL/ELT, CDC, streaming, batch, and event-driven architectures.
- Data modelling techniques including relational, dimensional, and analytical modelling.
- Data quality management, validation, reconciliation, and monitoring.
- Infrastructure as Code (Terraform/OpenTofu).
- CI/CD tools such as GitLab CI/CD and SHIP-HATS.
- Analytics, reporting, and data preparation for BI and AI/ML use cases.
- AWS and/or Azure native data, storage, analytics, and integration services.
Preferred Qualifications
- Experience with Singapore Government platforms such as TechPass, SHIP-HATS, SEED, and GCC.
- Familiarity with Government data classification and security requirements.
- AWS and/or Azure professional certifications.
- Experience designing hybrid on-premises and cloud data architectures.
- Experience with data lineage, metadata management, and data cataloguing solutions.
- Experience supporting enterprise operational systems, asset management, procurement, network, or master data management platforms.
