Data Engineer - 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.

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.

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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
• Terraform / OpenTofu
• GitLab CI/CD, SHIP-HATS or equivalent automated deployment practices
• Data preparation, analytical datasets, reporting, statistical analysis, and ML enablement