Data Engineer Data Engineering & Analytics (1 Year Contract)

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.