Geospatial Data Scientist / AWS Solutions Architect

Our client is looking for a Ukraine-based specialist to join a client engineering team as an embedded contractor. The role sits at the intersection of geospatial analysis and applied data engineering: building spatial data models and pipelines in Python, and supporting the cloud and streaming infrastructure those pipelines run on. This is a hands-on build role, not a research or academic position.

What You Will Work On

• Design and build geospatial data models and analytical pipelines using Python, applying spatial mathematics (coordinate systems, spatial statistics, geometric and network analysis) to real production data.
• Develop and maintain data pipelines that ingest, transform, and serve geospatial and streaming data at scale.
• Contribute to AWS architecture decisions for the data and analytics workloads this role owns, including compute, storage, and pipeline orchestration.
• Build and support Kafka-based streaming components where geospatial or event data needs to move in near real time.
• Support blockchain-related data workflows, such as ingesting or validating on-chain data as part of a broader data science pipeline.
• Work as a full member of the client's engineering team: standups, code review, sprint planning, shared ownership of outcomes.

Must-Have 

• Strong applied mathematics background in geospatial analysis, geodesy, or spatial statistics, demonstrated through work, not only coursework.
• Professional Python development experience, including data pipeline or data engineering work (not only notebooks/analysis scripts).
• Practical, hands-on AWS experience: has designed or significantly contributed to cloud architecture for a data or analytics workload, not only used managed services as an end user.

Nice to have

• AWS Solutions Architect certification (Associate or Professional).
• Production experience with Kafka or another streaming/event platform.
• Experience with blockchain data: on-chain data extraction, validation, or analytics.
• Familiarity with geospatial libraries and tooling (GeoPandas, Shapely, Rasterio, PostGIS, or similar).
• Experience with satellite imagery or remote sensing data.