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.