Data engineer

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 Sat Sure Sat Sure 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, Sat Sure 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., Postgre SQL with Post GIS extension) and No SQL databases (e.g., Mongo DB, Cassandra) to store and query spatial data efficiently. Geospatial Analysis Tools Integration: Integrate geospatial analysis tools and libraries (e.g., GDAL, Geo Pandas, 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). 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, Airflow) and cloud-based data platforms (e.g., AWS, Azure, Google Cloud Platform). Familiarity with geospatial data formats and standards (e.g., Geo JSON, 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., Post GIS). 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, Geo Parquet, Cloud native tools. 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, bereavement leaves Twice a year appraisal Interview Process: Intro call Assessment Presentation Interview rounds (ideally up to 3-4 rounds) Culture Round / HR round