Data engineer (Geospatial)
Summary
A full-time data engineering role in Bengaluru (5-10 yrs experience) building and owning large-scale production ETL/data pipelines with Python, SQL, and Spark/PySpark, with mandatory geospatial/location data experience. The engineer will be deployed on a project for a US-based oil & gas client and needs hands-on cloud (AWS/Azure/GCP) and production-support skills.
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.
