We are looking for a Data Engineer (Geospatial) to design, build, and maintain scalable data platforms that support geospatial analytics and spatial modelling initiatives within the public sector.
In this role, you will develop robust data pipelines and modern data architectures that enable advanced analytics for long-term infrastructure planning, demand forecasting, and evidence-based decision-making. You will work closely with data analysts, engineers, and business stakeholders to transform complex datasets into reliable, high-quality data assets that power geospatial applications and analytical models.
Key Responsibilities
- Collaborate with business users, data analysts, and stakeholders to gather requirements and translate business needs into scalable data engineering solutions.
- Design and implement modern data architectures, data models, and database structures that support analytical workloads while ensuring scalability, maintainability, and performance.
- Design end-to-end data pipelines to ingest, transform, and deliver data across multiple layers of the analytics platform.
- Develop, test, deploy, and maintain robust batch and real-time data processing pipelines using modern data engineering frameworks.
- Implement data transformation logic, validation rules, quality checks, monitoring, and error-handling mechanisms to ensure reliable data processing.
- Build automated workflows and pipeline orchestration processes to support efficient and scalable data operations.
- Monitor and optimise existing data pipelines and infrastructure to improve performance, reliability, scalability, and cost efficiency.
- Troubleshoot data quality issues, pipeline failures, and system bottlenecks while implementing continuous improvements.
- Apply data governance, security, metadata management, and lineage best practices across data platforms.
- Work closely with cross-functional teams to support geospatial analytics, spatial modelling, and business intelligence initiatives.
- Maintain technical documentation for data models, architectures, and pipeline implementations.
Requirements
- Minimum 3–5 years of experience in Data Engineering, Data Analytics, or a related technical field.
- Proven experience designing, developing, and maintaining scalable data pipelines and analytics platforms.
- Strong understanding of modern data architecture concepts, including Data Lakes, Data Warehouses, Lakehouses, and Data Mesh.
- Strong proficiency in Python, including data processing libraries such as Pandas and NumPy.
- Strong SQL skills with experience in data modelling, querying, transformation, and database optimisation.
- Solid understanding of data engineering principles, including data ingestion, ETL/ELT, data quality, orchestration, metadata management, and data governance.
- Experience designing and implementing scalable data models and database schemas.
- Strong analytical and problem-solving skills with the ability to troubleshoot complex data engineering challenges.
- Excellent communication skills with the ability to work effectively with both technical and non-technical stakeholders.
Preferred Skills
- Experience with Databricks and AWS cloud services.
- Familiarity with cloud-native data platforms, managed analytics services, and infrastructure-as-code practices.
- Experience with DevOps practices and CI/CD pipelines for data platforms.
- Knowledge of geospatial technologies such as ArcGIS, geospatial APIs, routing engines, spatial databases, or 2D/3D mapping technologies will be an advantage.
- Exposure to geospatial analytics, spatial modelling, or location intelligence solutions will be highly regarded.
Why Join
- Work on large-scale data platforms supporting impactful public sector initiatives.
- Build modern cloud-based data engineering solutions using contemporary technologies.
- Contribute to geospatial analytics and spatial modelling projects that support long-term planning and data-driven decision-making.
- Collaborate with multidisciplinary teams in an Agile, innovation-driven environment.
- Gain exposure to modern data architecture, cloud technologies, and large-scale analytics platforms.
