Data Engineer (GIS, ArcGIS, ESRI)

Key Roles and Responsibilities:

• Ideally a Data Analyst, Data Scientist, or Data Engineer with specific experience processing geospatial data and working with GIS, ArcGIS, ESRI.
• Initial project duration of 3 months with possibility of extension.
• Analyze, transform, and visualize geospatial data from GIS systems.
• Experience working with Python, MS SQL databases, and Talend for data processing.
• Design, develop, and maintain data pipelines for collecting, transforming, and loading data into various data stores.
• Build and maintain data warehousing and data lake solutions.
• Develop and deploy scalable data models to support diverse business requirements.
• Write efficient, scalable code in Python, Scala, or Java.
• Lead data solution design focusing on quality, automation, and performance.
• Own data pipelines feeding into the Data Platform, ensuring reliability and scalability.
• Ensure timely and fit-for-purpose data availability for business and analytics use.
• Collaborate with the Data Product Manager to align data requirements from business systems and endpoints.
• Communicate complex solutions clearly to both technical and non-technical stakeholders.
• Engage with stakeholders and clients to understand their data needs and provide actionable solutions.

Requirements:

• Proven experience with Geospatial data, GIS systems, ArcGIS, and ESRI.
• Track record of delivering large-scale data and analytics solutions in cloud environments.
• Hands-on expertise in implementing end-to-end data pipelines on AWS, including ETL, normalization, aggregation, warehousing, data lakes, and governance.
• Strong experience developing Data Warehouses and understanding modern data architectures such as Data Lake, Lakehouse, Data Mesh.
• Deep knowledge of data architecture, data modeling, and cost-effective data pipeline management.
• Familiarity with CI/CD driven data pipelines and infrastructure tools (e.g., Bitbucket).
• Experience with Agile delivery methodologies, including Scrum and Kanban.
• Supporting QA and user acceptance testing.
• Self-driven with continuous improvement mindset for data processes.