Systems Analyst (3D Data Engineer)

About the role

The Urban Redevelopment Authority (URA), Singapore's land use planning and conservation authority, is hiring a Systems Analyst (3D Data Engineer) within its Design and Planning Lab (DPLab). The lab works with planners, architects and policymakers to build digital solutions that improve the city's liveability, sustainability and resilience. As Systems Analyst, you will develop and maintain data pipelines and workflows that bring together geospatial and urban planning datasets from many sources, including the processing, transformation and integration of 2D and 3D geospatial data.

Key responsibilities

• Design, develop and maintain automated data pipelines and workflows for acquiring, ingesting, transforming, integrating and publishing 2D/3D geospatial and urban planning datasets.
• Develop and maintain data models, databases and ETL workflows using Python, SQL and other data engineering tools.
• Process, transform and convert 2D/3D geospatial datasets, including GIS, BIM and CAD data, for integration with enterprise geospatial platforms and 3D applications.
• Implement data quality checks, monitoring and error-handling processes.
• Integrate data solutions across cloud and on-premise environments using APIs and other integration technologies.
• Work with multi-disciplinary teams on data architecture, solution design, engineering, deployment, DevOps, troubleshooting, documentation and continuous improvement.

Required profile

• Understanding of data architecture and management, including storage, metadata, data quality, governance, security and lifecycle.
• Understanding of geospatial data concepts such as coordinate systems, spatial data formats, 2D/3D geometry and interoperability.
• Familiarity with cloud infrastructure, software engineering and DevOps practices, including version control, testing, deployment, CI/CD and monitoring.
• Ability to turn business and technical requirements into reliable, secure, scalable solutions, and to collaborate with multidisciplinary teams.

Required skills

• Hands-on data engineering: ETL/ELT, data modelling, relational databases, pipeline development, query and performance optimisation.
• Python and SQL for data pipelines.
• APIs, database connections and data exchange across cloud and on-premise environments.
• Geospatial and ArcGIS technologies (ArcGIS Enterprise, ArcPy, FME, PostGIS, Arcade, Attribute Rules, Parcel Fabric, Workflow Manager, Data Review, Knowledge Graph) and 3D formats (OBJ, IFC, CityGML, Multipatch, SLPK, OGC).
• Cloud and data platforms (AWS, Azure, Databricks) and DevOps tools (Git, CI/CD, Docker, Kubernetes, Terraform).
• System administration and scripting in Windows or Unix/Linux, monitoring tools (Azure Monitor, AWS CloudWatch, Splunk) and Agile practices (Jira, Confluence).