Data engineer (Geospatial)
Summary
Builds and manages scalable geospatial data pipelines and production ETL processes using Python, SQL, and Spark. The role involves working with location datasets like GPS and satellite imagery for a global consulting firm's oil & gas client.
Job Description
Company Overview
A global consulting and technology services firm focused on helping organizations drive business excellence through digital transformation, data & AI, enterprise architecture, managed services, and sustainability/ESG initiatives.
The company combines business strategy consulting with engineering and technology delivery, enabling organizations to modernize operations, leverage data-driven insights, and build sustainable business solutions.
Join the organization to work on diverse, high-impact digital and AI transformation projects as part of a global, growth-focused team.
Employment & Project:
This is a full-time opportunity with the organization, where you will be deployed on a project with a leading US-based organization in the oil & gas industry.
Role overview:
5-10 Years of Experience
- Core Data Engineering: 5–10 years with strong hands-on Python, SQL, Spark/PySpark and production ETL/data pipelines.
- Geospatial – Mandatory: Genuine experience working with geospatial/location datasets — coordinates, latitude/longitude, GPS, maps, satellite imagery, spatial datasets, etc.
- End-to-End Pipeline Ownership: Should have experience designing/building scalable data pipelines and architecture, including orchestration, data processing, storage and production support.
- Cloud & Scale: Any cloud (AWS/Azure/GCP) is acceptable, but should have hands-on experience processing large-scale data using cloud/data-platform technologies.
- Hands-on + Production Mindset: Looking for someone who remains technically hands-on and has experience with monitoring, data quality, troubleshooting, optimisation and production deployments.