As a Data Engineer specializing in geospatial data, your primary responsibility is to design, build, and maintain data infrastructure and systems that handle geospatial information effectively. You will work closely with cross-functional teams, including data scientists, geospatial analysts, and software engineers, to ensure that geospatial data is collected, processed, stored, and analyzed efficiently and accurately.
About SatSure
SatSure is a deep tech, decision Intelligence company that works primarily at the nexus of agriculture, infrastructure, and climate action creating an impact for the other millions, focusing on the developing world. We want to make insights from earth observation data accessible to all.
If you are interested in working in an environment that focuses on the impact on society, driven by cutting-edge technology, and where you will be free to work on innovative ideas and be creative with no hierarchies, SatSure is the place for you.
Key Responsibilities:
Data Pipeline Development: Design and implement robust data pipelines to acquire, ingest, clean, transform, and process geospatial data from various sources such as satellites, aerial, drones, and geolocation services.
Data Ingestion, Storage and Extraction: Develop data models and schemas tailored to geospatial data structures, ensuring optimal performance and scalability for storage and retrieval operations.
Spatial Database Management: Manage geospatial databases, including both traditional relational databases (e.g., PostgreSQL with PostGIS extension) and NoSQL databases (e.g., MongoDB, Cassandra) to store and query spatial data efficiently.
Geospatial Analysis Tools Integration: Integrate geospatial analysis tools and libraries (e.g., GDAL, GeoPandas, Fiona) into data processing pipelines and analytics workflows to perform spatial data analysis, visualization, and geoprocessing tasks.
Geospatial Data Visualization: Collaborate with data visualization specialists to create interactive maps, dashboards, and visualizations that effectively communicate geospatial insights and patterns to stakeholders. (frontend related)
Performance Optimization: Identify and address performance bottlenecks in data processing and storage systems, leveraging techniques such as indexing, partitioning, and parallelization to optimize geospatial data workflows.
Data Quality Assurance: Implement data quality checks and validation procedures to ensure the accuracy, completeness, and consistency of geospatial data throughout the data lifecycle.
Geospatial Data Governance: Establish data governance policies and standards specific to geospatial data, including metadata management, data privacy, and compliance with geospatial regulations and standards (e.g., INSPIRE, OGC).
Distributed Geospatial Processing: Design and maintain large-scale geospatial data pipelines using Apache Spark with Apache Sedona for efficient distributed spatial processing.
Lakehouse & Table Formats: Implement and manage geospatial datasets using modern table formats such as Apache Iceberg, enabling schema evolution, time travel, and scalable analytics.
Scalable Compute Optimization: Optimize distributed geospatial workloads through partitioning strategies, spatial indexing, and compute-aware storage layouts.
Collaboration and Communication: Collaborate with cross-functional teams to understand geospatial data requirements and provide technical expertise and support. Communicate findings, insights, and technical solutions effectively to both technical and non-technical stakeholders.
Requirements:
Must-have:
Bachelor's or Master's degree in Computer Science or a related field.
3-5 years of experience working in the field and deploying the pipelines in production.
Strong programming skills in languages such as Python, Java, or Scala, with experience in geospatial libraries and frameworks (e.g., Rasterio, Shapely).
Experience with distributed computing frameworks (e.g., Apache Spark (sedona), Airflow) and cloud-based data platforms (e.g., AWS, Azure, Google Cloud Platform).
Practical exposure to Apache Iceberg or similar table formats in production-grade data platforms.
Familiarity with geospatial data formats and standards (e.g., GeoJSON, Shapefile, KML) and geospatial data visualization tools (e.g., Mapbox, Leaflet, Tableau).
Strong analytical and problem-solving skills, with the ability to work with large and complex geospatial datasets.
Good-to-have:
Proficiency in SQL and experience with geospatial extensions for relational databases (e.g., PostGIS).
Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.
Nice to have experience with geospatial libraries such as Rasterio, Xarray, Geopandas, and GDAL.
Nice to have Knowledge of distributed computing frameworks such as Dask.
STAC, GeoParquet, Cloud native tools.
Experience with Dask for parallel and out-of-core geospatial data processing.
Familiarity with Ray for distributed execution, orchestration, or ML/data workloads.
Experience designing lakehouse architectures for geospatial or raster-heavy datasets.
Productionising Data Science code.
The role of a Data Engineer for Geospatial Data is crucial in enabling organizations to leverage the power of geospatial information for various applications, including urban planning, environmental monitoring, transportation, agriculture, and emergency response.
Benefits:
Medical Health Cover for you and your family, including unlimited online doctor consultations
Access to mental health experts for you and your family
Dedicated allowances for learning and skill development
Comprehensive leave policy with casual leaves, paid leaves, marriage leaves, and bereavement leaves
Interview Process:
Intro call
Assessment
Presentation
Interview rounds (ideally up to 3-4 rounds)
Culture Round / HR round
