Data Engineering & Analytics Engineer (Public Sector)

1-year contract, renewable
Government project
Hybrid work arrangement

We are looking for a Data Engineering & Analytics Engineer to design, build, and operate the data pipelines and data products supporting the future SSOE platform.

You will transform data from enterprise systems, operational platforms, applications, and infrastructure into trusted and usable data products for applications, operational reporting, analytics, capacity planning, and decision-making.

The role spans on-premise, GCC and hybrid environments, with an emphasis on production-grade data engineering using cloud-native and managed data capabilities.

What You Will Be Working On

As a Data Engineering & Analytics Engineer, you will own the data lifecycle from source systems through ingestion, transformation, modelling, quality, and serving.

You will build pipelines that extract and ingest data from enterprise and operational systems, transform it into consistent and trusted datasets, and make that data available to applications, dashboards, reporting, analytics, and machine-learning use cases.

You will work closely with the Logging & Data Platform Engineer on shared platform capabilities and with Software Engineers and other consumers to define reliable data interfaces and products.

Key Responsibilities

Data Pipeline Engineering

Design, build, and operate production-grade data pipelines for data extraction, ingestion, transformation, and loading (ETL/ELT)
Integrate data from on-premises systems, enterprise applications, APIs, databases, SaaS platforms, files, streams, cloud services, and other operational data sources
Develop batch, incremental, change-data-capture (CDC), streaming, and event-driven ingestion patterns based on source-system and business requirements
Build transformation pipelines that clean, enrich, standardise, join, aggregate, and structure raw data into trusted datasets
Design secure and resilient mechanisms for transferring and synchronising data between on-premises, GCC, AWS, Azure, and other approved environments
Design pipelines for failure handling, retry, recovery, idempotency, scalability, and changing data volumes
Automate pipeline deployment, configuration, testing, and operation

Data Architecture & Modelling

Design and maintain cloud-native and hybrid data stores, data lakes, and analytical datasets
Develop data models that provide consistent representations of enterprise, operational, and asset information
Define schemas and data contracts between data producers and downstream consumers
Design data structures appropriate for operational applications, reporting, analytics, and machine-learning workloads
Apply backwards-compatible schema changes and coordinate changes that may affect downstream consumers
Maintain data lineage and metadata so datasets are traceable and discoverable
Work with platform and application teams to define appropriate data-serving and integration patterns

Data Quality & Reliability

Implement automated data validation, reconciliation, completeness, consistency, and quality controls throughout the pipeline lifecycle
Monitor data freshness, pipeline health, processing latency, and data-quality indicators
Detect and investigate ingestion failures, source-system changes, data-quality anomalies, and reconciliation differences
Prevent invalid or incomplete data from silently propagating to downstream consumers
Define appropriate SLOs for data freshness, availability, and pipeline reliability
Build monitoring, alerting, error handling, and recovery into data pipelines from the outset

Analytics & Data Products

Build trusted datasets and reusable data products for applications, dashboards, operational reporting, and analytics
Develop datasets supporting asset intelligence, operational visibility, capacity planning, trend analysis, and decision-making
Enable advanced analytics and machine-learning use cases using cloud-native data, analytics, and AI/ML capabilities
Work with users and stakeholders to translate operational questions into appropriate datasets, metrics, and analytical products
Support exploratory analysis and prototyping where required before operationalising successful approaches
Ensure analytical outputs are based on governed, traceable, and reproducible data

Data Integration

Design data architectures spanning on-premise infrastructure and cloud platforms
Integrate traditional enterprise systems with modern cloud-native data capabilities
Design for connectivity constraints, network boundaries, security zones, and data-residency requirements
Implement appropriate buffering, checkpointing, retry, and reconciliation where data crosses environment boundaries
Select appropriate integration patterns based on data volume, latency, source-system capability, and operational requirements
Work with infrastructure, network, security, and platform teams to establish secure data flows

Security & Governance

Ensure data is collected, transmitted, stored, processed, and accessed according to applicable security requirements
Enforce appropriate access controls and least-privilege principles for data platforms and pipelines
Ensure sensitive information is appropriately classified and protected throughout the data lifecycle
Maintain auditability and traceability of data-processing activities
Apply retention, archival, lifecycle, and deletion requirements to data products
Participate in security, architecture, data-governance, and operational-readiness reviews

Reliability & Operations

Operate and support production data pipelines and data products
Participate in operational support and on-call responsibilities for owned services
Investigate production incidents and contribute to root-cause analysis and preventative improvements
Monitor pipeline performance, capacity, reliability, and cost
Maintain architecture documentation, data definitions, operational procedures, and runbooks
Continuously improve pipeline automation, reliability, performance, and maintainability

What We Are Looking For

Experience

Minimum 3–5 years of experience in data engineering, cloud data engineering, analytics engineering, software engineering, or a related discipline
At least 2 years of hands-on experience designing, building, and operating production-grade data pipelines
Demonstrated experience with data extraction, ingestion, ETL/ELT, transformation, data modelling, and data quality
Experience using AWS and/or Azure native data capabilities
Experience integrating data from APIs, databases, enterprise systems, files, or streaming sources
Experience implementing batch, incremental, CDC, and/or event-driven data pipelines
Experience working with on-premises and/or cloud environments, with an understanding of hybrid integration patterns
Experience applying software-engineering practices such as version control, automated testing, CI/CD, monitoring, and Infrastructure as Code to data solutions

Technical Skills

Cloud: AWS/Azure-native logging, streaming, storage, search and data services
On-Premise: Enterprise servers, networks, applications, databases, virtualised infrastructure, and log sources
Data Engineering: Python, SQL, ETL/ELT, batch, incremental, CDC, streaming, and event-driven patterns
Data Modelling: Relational, dimensional, analytical, and domain-oriented data modelling
Data Quality: Validation, reconciliation, quality monitoring, lineage, and anomaly detection
Infrastructure as Code: Terraform / OpenTofu
CI/CD: GitLab CI/CD, SHIP-HATS or equivalent automated deployment practices
Analytics: Data preparation, analytical datasets, reporting, statistical analysis, and ML enablement

Engineering Practices

Treats data pipelines and data products as production software, not one-off scripts
Keeps pipeline code, schemas, infrastructure, and configuration under version control
Uses automated testing, CI/CD, Infrastructure as Code, and monitoring
Designs pipelines for failure, retry, idempotency, scalability, and changing workloads
Validates data at ingestion and transformation boundaries
Establishes explicit data contracts between producers and consumers
Understands when to use managed cloud-native capabilities rather than unnecessarily building and operating infrastructure
Considers downstream consumers before making schema or behavioural changes
Automates repeatable data-processing and operational activities
Balances technical excellence with pragmatic delivery and operational sustainability

Nice to Have

Experience with Singapore Government platforms such as TechPass, SHIP-HATS, SEED, and GCC
Familiarity with OC/SN data-classification requirements
AWS or Azure cloud certifications
Experience designing data architectures spanning on-premises and cloud environments
Experience building data products consumed by applications, dashboards, operational teams, or leadership
Experience with managed cloud analytics and AI/ML capabilities
Experience with data lineage, metadata management, and data cataloguing
Experience working with enterprise asset management, MDM, network, procurement, or operational systems

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