Data Engineer

RAPSYS TECHNOLOGIES PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

RAPSYS TECHNOLOGIES PTE. LTD. is hiring a Data Engineer to design data models, build transformation pipelines, and deliver dashboards that support cyber asset visibility, vulnerability prioritisation, and incident readiness. You will collaborate with analysts to shape data narratives and ensure data quality across diverse sources.

The role focuses on modeling, ETL/ELT processes, and dashboard development using BI tools, with an emphasis on clean, reliable outputs and scalable data architectures.

Qualifications

• Minimum 3 years of experience in data engineering, analytics engineering, or BI development.
• Strong proficiency in SQL and data transformation frameworks (dbt, Spark).
• Hands-on experience building dashboards with BI tools (Power BI, Tableau, Grafana, Superset).
• Experience designing data models for operational/analytical use cases (star schemas, entity resolution, graph-based assets).
• Understanding of data quality practices including validation, deduplication, lineage, and monitoring.

Responsibilities

• Data Modelling and Transformation: design/maintain data models unifying cyber asset information from diverse sources.
• Build and maintain transformation pipelines to clean, normalise, enrich, and relate ingested data into an asset inventory.
• Establish data quality standards: deduplication, completeness checks, schema validation, lineage tracking.
• Evolve the data model as new sources are onboarded with minimal disruption to dashboards.
• Dashboard and Visualisation Development: build dashboards addressing agency use cases including asset visibility and vulnerability prioritisation.
• Collaborate with Business Analyst to develop data narratives and identify key metrics, views, and drill-downs.
• Iterate dashboard designs based on feedback, balancing clarity with analytical depth.
• Platform Data Operations: validate data ingestion with Platform Infrastructure and monitor pipeline health for data quality issues.
• Optimise query performance and data refresh schedules to keep dashboards current.
• Document data models, transformation logic, and dashboard specifications for continuity.

Skills

SQL

Data transformation frameworks

dbt

Apache Spark

Power BI

Tableau

Grafana

Superset

Data Modeling

Data Quality

Tools

dbt

Apache Spark

Job description

Requirements

Essential

• Minimum 3 years of experience in data engineering, analytics engineering, orbusiness intelligence development
• Strong proficiency in SQL and experience with data transformation frameworks(e.g., dbt, Apache Spark, or equivalent)
• Hands-on experience building dashboards and visualizations with BI tools (e.g.,Power BI, Tableau, Grafana, Superset, or platform-native tooling)
• Experience designing data models for operational or analytical use cases star schemas, entity resolution, or graph-based asset relationships Ability to work with messy, heterogeneous data from multiple sources and produce clean, reliable outputs
• Understanding of data quality practices including validation, deduplication, lineage, and monitoring

Key Responsibilities

• Data Modelling and Transformation
• Design and maintain data models that unify cyber asset information from diverse
• Build and maintain transformation pipelines that clean, normalise, enrich, andrelate ingested data into a coherent asset inventory
• Establish and enforce data quality standards deduplication, completenesschecks, schema validation, and lineage tracking
• Evolve the data model as new data sources are onboarded, ensuring backwardcompatibility and minimal disruption to existing dashboards
• Dashboard and Visualisation Development
• Build dashboards that address agency-specific use cases including asset visibility,

• vulnerability prioritisation, patch tracking, and incident response readiness
• Collaborate with the Business Analyst to develop compelling data narratives
• selecting the right metrics, views, and drill-downs that connect data to agency decision-making
• Iterate on dashboard designs based on agency feedback, balancing clarity with analytical depth
• Maintain and update existing dashboards as underlying data models or agency requirements evolve Platform Data Operations
• Validate successful data ingestion in coordination with the Platform Infrastructure
• Engineer confirming completeness, freshness, and schema conformanceMonitor data pipeline health, investigate anomalies, and resolve data quality issues
• Optimise query performance and data refresh schedules to ensure dashboards remain responsive and current Document data models, transformation logic, and dashboard specifications for operational continuity