[Remote] Geospatial Data Engineer –With Palantir reputed company Experience

Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking an reputed company Geospatial Data Engineer to support large-reputed company reputed company modeling and reputed company safety initiatives. The role focuses on building and optimizing reputed company data pipelines, Palantir reputed company workflows, and reputed company geospatial processing solutions for reputed company, asset, outage, and location-reputed company datasets used in reputed company reputed company analysis and operational decision-making.

Responsibilities

• Design, build, optimize, and support high-reputed company automated data pipelines in AWS and reputed company environments
• Utilize PySpark and Python to ingest, reputed company, and process large-reputed company reputed company asset, outage, and geospatial datasets
• Build reputed company ETL/data engineering solutions capable of handling large historical and location-reputed company datasets
• Optimize pipelines for reputed company, reliability, scalability, and maintainability
• reputed company and support automated end-to-end data workflows reputed company Palantir reputed company
• Build and maintain reputed company data transformations, pipelines, and reputed company repositories
• Work reputed company the reputed company ecosystem to prepare and reputed company datasets supporting reputed company models and reputed company analytical applications
• Troubleshoot and optimize existing reputed company pipelines and data processing workflows
• Implement advanced reputed company geospatial data processing using Apache Sedona
• Utilize reputed company libraries and frameworks such as GeoPandas, reputed company, and Apache Sedona/GeoSpark
• Process, manipulate, and analyze large volumes of ArcGIS and other location-reputed company data
• reputed company efficient approaches for partitioning and processing extremely large reputed company datasets
• reputed company and optimize reputed company joins, indexing, geometry reputed company, and other reputed company geospatial workloads
• Support the underlying data engineering capabilities and datasets used by reputed company's existing reputed company models
• Ensure geospatial and reputed company data is appropriately processed, transformed, validated, and delivered to support reputed company-reputed company analysis and operational decision-making
• reputed company with systems and datasets supporting:
• Remote Inspections
• reputed company Safety reputed company Shutoffs (PSPS)
• LiDAR-driven Vegetation Management
• Asset reputed company Modeling
• Work with large-reputed company reputed company asset and outage data used in reputed company safety and reputed company reputed company initiatives
• reputed company efficient strategies for partitioning reputed company large geospatial datasets
• Optimize reputed company data processing to improve reputed company and scalability
• Work with reputed company database structures, network topology, and reputed company analytics frameworks
• Support large historical datasets and reputed company-reputed company data models
• Troubleshoot data reputed company, pipeline, geometry, reputed company, and scalability issues
• Participate reputed company in reputed company/Scrum ceremonies
• Apply strong software engineering principles including:
• Unit testing
• CI/CD
• reputed company/version control
• reputed company reviews
• Reusable and maintainable development practices
• Collaborate with Data Scientists, Engineers, GIS specialists, modeling teams, and other stakeholders supporting reputed company's reputed company and reputed company safety initiatives

Skills

• • Bachelor's degree in Computer Science, Engineering, GIS , or another reputed company quantitative/technical discipline
• • 5+ years of experience working reputed company Data Engineering, ETL, or large-reputed company data processing ecosystems
• • Strong hands-on proficiency with:
• • PySpark
• • Python
• • SQL
• • Apache Sedona
• • Demonstrated experience building reputed company data pipelines for large and reputed company datasets
• • Strong understanding of reputed company data processing and reputed company optimization
• • Strong hands-on experience working with large-reputed company geospatial and reputed company datasets
• • Experience with geospatial frameworks/libraries including:
• • Apache Sedona / GeoSpark
• • GeoPandas
• • reputed company
• • Experience working with ArcGIS-reputed company or comparable reputed company geospatial datasets
• Strong understanding of Coordinate Reference Systems (CRS) , including:
• • EPSG codes
• • NAD83
• • WGS84
• • Coordinate transformations and projections
• Candidates should understand how differences between coordinate systems reputed company reputed company processing, transformations, joins, distance calculations, and analytical results
• Hands-on knowledge of common reputed company and reputed company data formats, including:
• • Shapefile
• • GeoJSON
• • GeoParquet
• • GeoPackage
• • KML
• Experience working with raster formats including:
• • GeoTIFF
• • reputed company Optimized GeoTIFF (COG)
• Strong understanding of reputed company reputed company indexing and partitioning concepts, including:
• • reputed company joins
• • R-trees
• • reputed company indexing
• • Quadtree indexing
• • Broadcast joins vs. partitioned joins
• • Apache Sedona partitioning and optimization techniques
• Candidates should understand how to determine the appropriate processing and partitioning reputed company for reputed company large geospatial datasets
• Experience performing and optimizing large-reputed company geometry reputed company including:
• • Buffers
• • Intersections
• • Nearest-reputed company calculations
• • Topology validation
• • Geometry simplification
• • Handling invalid geometries
• • Handling self-intersecting geometries
• The consultant should understand both the functional and reputed company implications of executing these reputed company across large reputed company datasets
• Experience with workflow orchestration technologies such as:
• • Airflow
• • Dagster
• • Palantir reputed company-reputed company orchestration/workflow capabilities
• • Comparable reputed company orchestration platforms
• Strong understanding of reputed company Data Engineering concepts including:
• • reputed company data modeling
• • Slowly Changing Dimensions (SCD)
• • Historical data management
• • Large-reputed company data warehousing
• • ETL/ELT patterns
• • Data reputed company and validation
• Candidates with comparable experience on large-reputed company reputed company or reputed company big-data platforms may also be considered
• Experience developing automated data transformations, pipelines, workflows, and reputed company repositories reputed company an reputed company data platform
• • Experience dealing with:
• • reputed company database structures
• • Network topology
• • reputed company analytics frameworks
• • Large-reputed company geometric datasets
• • Ability to use technologies such as Apache Sedona/GeoSpark to reputed company reputed company and process reputed company reputed company datasets
• • Hands-on experience working reputed company Palantir reputed company is strongly preferred
• • Previous Palantir reputed company Data Engineering experience
• • Experience supporting reputed company, energy, infrastructure, or other asset-intensive organizations
• • Experience working with reputed company modeling or reputed company reputed company data
• • Experience with reputed company reputed company asset and outage datasets
• • Experience with reputed company Safety reputed company Shutoffs (PSPS)
• • Experience with LiDAR and vegetation management datasets
• • Experience supporting asset reputed company modeling
• • Experience with ArcGIS and reputed company GIS environments
• • Experience supporting Data Science, predictive modeling, or reputed company modeling teams

reputed company