The Role
We are looking for a Data Engineer to design, build, and operate production-grade data pipelines and data products across cloud and hybrid environments.
You will work across the full data lifecycle — from ingestion and transformation through modelling, data quality, governance, and serving — turning data from enterprise and operational systems into trusted datasets that support applications, dashboards, reporting, analytics, and machine-learning use cases.
You will work closely with Software Engineers, Platform Engineers, and other stakeholders to establish reliable data flows, interfaces, and reusable data products.
What You'll Be Doing
- Design, build, and operate production-grade ETL/ELT data pipelines for extraction, ingestion, transformation, and serving.
- Integrate data from APIs, databases, enterprise applications, SaaS platforms, files, cloud services, and streaming sources.
- Develop batch, incremental, CDC, streaming, and event-driven data pipelines.
- Build transformation pipelines to clean, enrich, standardise, aggregate, and structure raw data into trusted datasets.
- Design and maintain cloud-native and hybrid data stores, data lakes, analytical datasets, schemas, and data models.
- Define data contracts and reliable integration patterns between source systems and downstream consumers.
- Implement automated data validation, reconciliation, quality monitoring, lineage, and anomaly detection.
- Monitor data freshness, pipeline health, processing latency, failures, and data-quality indicators.
- Design resilient pipelines with appropriate retry, recovery, checkpointing, idempotency, and failure handling.
- Build reusable data products for applications, dashboards, operational reporting, analytics, and machine-learning use cases.
- Design secure data flows across on-premise, GCC, AWS, Azure, and hybrid environments.
- Automate pipeline infrastructure, deployment, testing, and monitoring using Infrastructure as Code and CI/CD.
- Support production data pipelines, troubleshoot incidents, and continuously improve reliability, scalability, performance, and cost.
- Maintain technical documentation, data definitions, operational procedures, and runbooks.
What We're Looking For
- 3–5+ years of experience in Data Engineering, Cloud Data Engineering, Analytics Engineering, Software Engineering, or a related field.
- At least 2 years of hands-on experience designing, building, and operating production-grade data pipelines.
- Strong hands-on experience with Python and SQL.
- Experience with ETL/ELT, batch, incremental, CDC, streaming and/or event-driven data processing.
- Experience with AWS and/or Azure native data, storage, streaming, and analytics services.
- Experience integrating data from APIs, databases, enterprise systems, files, and/or streaming sources.
- Strong understanding of relational, dimensional, analytical, and domain-oriented data modelling.
- Experience with data validation, reconciliation, quality monitoring, lineage, and anomaly detection.
- Experience working across on-premise and cloud environments, including hybrid integration patterns.
- Experience applying software engineering practices including version control, automated testing, CI/CD, monitoring, and Infrastructure as Code.
- Hands-on experience with Terraform and/or OpenTofu.
- Experience with GitLab CI/CD, SHIP-HATS, or equivalent automated deployment practices.
- Good understanding of data security, access controls, governance, and data lifecycle management.
- Strong engineering mindset with a focus on building maintainable, reliable, and production-ready data solutions.
Nice to Have
- Experience working with Singapore Government environments and platforms, including GCC, TechPass, SHIP-HATS, and SEED.
- Familiarity with OC/SN data-classification requirements.
- AWS and/or Azure cloud certifications.
- Experience designing data architectures spanning on-premise and cloud environments.
- Experience with data lineage, metadata management, and data cataloguing.
- Experience with cloud-native analytics and AI/ML capabilities.
- Experience building data products consumed by applications, dashboards, operational teams, or business stakeholders.
- Experience working with enterprise asset management, MDM, network, procurement, or other operational systems.
