Senior Geospatial Data Engineer

What you will do:

• Architect, design, and maintain robust, scalable data pipelines and infrastructures for geospatial and big data applications maintaining a focus on performance and the ultimate end-user product experience.
• Lead the development and optimization of ETL processes for ingesting, cleaning, transforming, and storing large volumes of geospatial and tabular data.
• Design, build, and interact with API-driven, service-to-service web services (using FastAPI, Litestar, Flask, etc.) to enable integration across a suite of products.
• Collaborate with backend and platform engineers to ensure secure, reliable, and scalable service-to-service communication.
• Translate complex analytics and business questions into actionable, production-grade data solutions.
• Collaborate closely with data scientists, analysts, and business stakeholders to deliver high-impact data products.
• Drive the adoption and optimization of cloud-based data solutions (e.g., GCP, AWS, Azure).
• Ensure data quality, integrity, and security across all stages of the data lifecycle.
• Mentor and provide technical guidance to junior data engineers and team members.
• Communicate technical details and insights clearly to both technical and non-technical audiences, including leadership.
• Proactively recommend and implement improvements to existing data infrastructure and software programs.
• Stay current with industry trends and emerging technologies in geospatial data engineering.

What you will bring:

• An excitement and dedication towards manifesting real and measurable impact for customers and clients and a dedication to being a team player towards achievement of those outcomes.
• Experience in software development, data engineering, or big data roles, preferably with a focus on geospatial data.
• Experience building solutions with Python.
• Experience with relational databases (e.g., SQL), including advanced query building, data extraction, and manipulation.
• Experience architecting and optimizing cloud-based data solutions (preferably GCP, AWS, or Azure).
• Deep experience with big data technologies such as Hadoop, Spark, MapReduce, or Kafka.
• Experience integrating with API-driven, service-to-service web services.
• Demonstrated ability to lead projects, mentor team members, and drive technical decisions.
• Strong problem-solving skills, resourcefulness, and ability to work independently or collaboratively.
• Excellent organizational, interpersonal, and communication skills.

What will make you stand out:

• Expertise with geospatial libraries and tools (e.g., GDAL, PDAL, PostGIS, GeoPandas, Shapely).
• Experience deploying and scaling machine learning (ML) models/algorithms in production.
• Strong experience with geospatial analytics and working with geospatial data formats (e.g., LAS, LAZ, COPC, GeoTIFF, Shapefiles).
• Experience leading teams in integrating and scaling complex ML/Deep Learning (DL) algorithms.
• Experience working with LiDAR data and deriving real-world insights from point clouds.
• Experience with ESRI products (ArcGIS Pro, ArcGIS Online, ArcGIS Enterprise) or other GIS platforms.
• Experience with data streaming, real-time data processing, or cloud-native geospatial solutions.
• Cloud certifications (e.g., Google Cloud Professional Data Engineer, AWS Certified Data Analytics).
• Experience with OAuth, authentication, and API key management for secure service-to-service communication.