About the Role :

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. The ideal candidate should have experience working with geospatial/spatial data such as latitude/longitude coordinates, GPS/location data, maps, spatial datasets, or location-based datasets. Experience in geospatial data processing is mandatory for this role.

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 AWS, Azure, or GCP.

- Work with data lakes, data warehouses, distributed processing frameworks, and modern data architectures.

- Implement workflow orchestration using tools such as Apache Airflow, AWS Step Functions, Azure Data Factory, Google Cloud Composer, or equivalent.

- 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 - AWS, Azure, or GCP.

- Good understanding of data lakes, data warehouses, distributed data processing, and modern data architectures.

- Experience with at least one workflow orchestration tool such as Airflow, AWS Step Functions, Azure Data Factory, Google Cloud Composer, or equivalent.

- 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 be in latitude/longitude and maps.