Azure Data Engineer (Geospatial)

We are looking for an experienced Data Engineer with strong hands-on expertise in Python, SQL, Spark/PySpark, and geospatial data processing to build and maintain scalable, production-grade data pipelines. You will work closely with Data Scientists, Analysts, and Engineering teams to build reliable data platforms and prepare high-quality datasets for analytics and downstream applications.

Responsibilities:

• Design, build, and maintain scalable ETL/ELT data pipelines using Python, SQL, and Spark/PySpark.
• Process and transform large-scale geospatial and non-geospatial datasets from multiple sources.
• Work extensively with latitude/longitude, GPS, coordinates, maps, location data, and spatial datasets.
• Perform geospatial data processing and analysis, including spatial joins, Proximity analysis, Distance calculations, Location-based aggregations, and coordinate and location transformations.
• Integrate geospatial data with structured and unstructured data sources.
• Build and optimise data pipelines for performance, reliability, scalability, and maintainability.
• Develop cloud-based data solutions using Azure.
• Work with data lakes, data warehouses, distributed processing frameworks, and modern data architectures.
• Implement workflow orchestration using tools such as Apache Airflow and Azure Data Factory.
• Implement data quality checks, testing, monitoring, and pipeline observability.
• Troubleshoot pipeline failures, data inconsistencies, and performance issues.
• Collaborate with Data Scientists, Data Analysts, and Engineering teams to prepare high-quality datasets for analytics and downstream applications.
• Follow best practices around data security, governance, deployment, and production operations.
• Participate in Agile development practices, code reviews, and technical discussions.

Requirements:

• 5-10 years of experience in Data Engineering or a closely related role.
• Strong hands-on experience with Python and SQL for data processing and pipeline development.
• Experience working with geospatial/location datasets, coordinates, latitude/longitude, GPS, maps, satellite imagery, spatial datasets, etc.
• Hands-on experience with Apache Spark/PySpark for processing large-scale datasets.
• Hands-on experience working with geospatial/spatial data is mandatory.
• Experience building and maintaining production-grade ETL/ELT data pipelines.
• Experience with at least one major cloud platform, Azure.
• Good understanding of data lakes, data warehouses, distributed data processing, and modern data architectures.
• Experience with workflow orchestration tools such as Azure Data Factory
• Understanding of data quality, testing, monitoring, security, and governance practices.
• Strong problem-solving and communication skills, with the ability to work effectively in an Agile engineering environment.
• In terms of geospatial data, experience should include latitude/longitude and maps.