Data Engineer (Data Engineering & Analytics) - A26321

Activate Interactive Pte Ltd (“Activate”) is a leading technology consultancy headquartered in Singapore with a presence in Malaysia and Indonesia. Our clients are empowered with quality, cost-effective, and impactful end-to-end application development, like mobile and web applications, and cloud technology that remove technology roadblocks and increase their business efficiency.

We believe in positively impacting the lives of people around us and the environment we live in through the use of technology. Hence, we are committed to providing a conducive environment for all employees to realise their full potential, who in turn have the opportunity to continuously drive innovation.

We are searching for our next team members to join our growing team.

If you love the idea of being part of a growing company with exciting prospects in mobile and web technologies that create positive impact on people’s lives, then we would love to hear from you.

Co-Development Business Unit is looking for Data Engineer (Data Engineering & Analytics)

This is a 1 - year contract role.

Internal Code: A26321

Digital Excellence & Products Division (DXD) is a GovTech team within the Ministry of Education (MOE). DXD sits at the intersection of technology, design, and education, building meaningful products, platforms, and digital services that improve teaching, learning, school operations, and the experience of students, teachers, and school leaders.

We are looking for a DevSecOps & Engineering Enablement Engineer to establish and operate a consistent, secure, and reliable process for moving code from development to production for the future SSOE platform.

You will centrally manage the tools, pipelines, standards, and automation that engineering teams use to build, test, review, secure, and deploy their code.

The objective is to ensure that code progressing towards production is not only deployed successfully, but is also functionally working, secure, tested, and of the required quality. Relevant security, testing, and quality gates should be built directly into the delivery process and applied consistently across engineering teams.

You will also drive the responsible use of AI within the software development lifecycle, including AI-assisted code review, testing, security analysis, documentation, and engineering feedback.

What will you do?

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.

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

Requirements

What are we looking for?

• 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
• 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
• 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
• AWS/Azure-native logging, streaming, storage, search and data services
• Enterprise servers, networks, applications, databases, virtualised infrastructure, and log sources
• Python, SQL, ETL/ELT, batch, incremental, CDC, streaming, and event-driven patterns
• Relational, dimensional, analytical, and domain-oriented data modelling
• 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
• Data preparation, analytical datasets, reporting, statistical analysis, and ML enablement

Benefits

What do we offer in return?

Competitive Compensation: Market competitive salary and variable performance bonus aligned to your skills, impact, and contribution.

Benefits: Outpatient medical, specialist medical coverage and generous customisable flexi benefits, or flexi allowance; life and health insurance, thoughtful perks like special occasions “red packet” and CNY goodies, etc.

Employee Wellness: Support for your physical, mental, and overall well-being through year-round initiatives.

Growth & Development: Learning programmes, certification support, and a dedicated staff development budget. (We are a “SHRI 2025 Gold winner” in “Learning & Development; Coaching & Mentoring”)

Career Progression: Structured career pathways that enable you to grow along a technical/domain expert track or a leadership track.

Competency Framework: A structured and practical framework to support you to develop, perform, and succeed.

Flexible Work Arrangement: Staff may choose to work flexi-place, flexi-time, and flexi-load based on existing framework

Why you'll love working with us?

If you are looking for opportunities to collaborate with leading industry experts and be surrounded by highly motivated and talented peers, we welcome you to join us. We provide all employees with equal opportunities to grow and develop with us. We believe your success is our success.

Does it sound like something you are interested in exploring further? Please be in touch with our team for an initial chat.

Activate Interactive Singapore is an equal opportunity employer. Employment decisions will be based on merit, qualifications and abilities. Activate Interactive Pte Ltd does not discriminate in employment opportunities or practices on the basis of race, colour, religion, gender, sexuality, national origin, age, disability, marital status or any other characteristics protected by law.

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