Senior Data Engineer, Geospatial
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- Espoo, Helsinki
- Vakituinen
- Täyspäiväinen
- Senior Data Engineer, Geospatial
- Location: Espoo, Finland
- Department: Solutions
- Reports to: Director of Product Engineering
- Employment type: Permanent
- Workplace model: Hybrid
- Employment is subject to applicable security screening (incl. SUPO, where required)
- Pipeline Scaling: Extend the existing geospatial data pipeline to cover millions of US properties across several states, using established ingest patterns and adding new data sources
- Data Integration: Integrate terrain, hydrographic, land cover, weather, and third-party risk datasets; build and maintain spatial joins at the property scale with quality and lineage tracking
- Performance Tuning: Configure and tune databases for large-scale spatial workloads, indexes, parallelism, memory, and connection management
- Frictionless Data Delivery: Automate versioned, analysis-ready data flows that allow data scientists to move from experimental hypothesis to production with zero friction
- Data Quality: Maintain coverage monitoring and consistency checks across geographies and data sources
- Workflow Reliability: Improve the scheduling, orchestration, and observability of ingest and export pipelines to support repeatable production operations across geographies and data sources
- Analytical Architecture & Dataset Design: Proven experience designing and building analysis-ready data structures. You know how to transform raw, noisy geospatial sources into clean, versioned, and performant datasets (e.g., Star Schemas, Feature Tables, or Partitioned Parquet) that allow researchers to iterate without friction
- Geospatial & Performance Engineering: 5+ years of professional experience with PostgreSQL / PostGIS in production. You possess deep knowledge of spatial indexing, complex joins, and the performance trade-offs between database-centric and distributed execution patterns for large-scale raster and vector processing
- Software Craftsmanship & Code Ownership: Confidence in reading and extending complex Python codebases. You value incremental delivery and have the engineering discipline to ship production-grade code that is testable and maintainable from day one
- Data Modeling & Integrity Mastery: Hands-on expertise with common geospatial models and cloud-native formats (Cloud Optimized GeoTIFFs, Geoparquet, STAC). You understand the "physicality" of the data reprojections, tiling, and transformations and how to structure it to ensure long-term data integrity and lineage
- Modern Tooling & Velocity: A pragmatic and expert-level approach to using AI-assisted tools (e.g., Cursor, Claude, Copilot). You use these to accelerate routine engineering (boilerplate, unit tests, refactoring), allowing you to focus on high-level architectural decisions
- Infrastructure & AWS: Proficiency with AWS (S3, RDS/Aurora, EC2) and scaling database workloads. You have experience managing high-volume data flows where operational stability is as important as the speed of the initial delivery.
- Education: Master's degree in Computer Science, Geoinformatics, or a related quantitative field or equivalent depth earned through building real-world systems at scale
- Experience with flood, climate, or natural hazard data (FEMA, NOAA, USGS)
- Deep familiarity with classic and modern geospatial stack - from GDAL/OGR to rioxarray
- Experience with distributed data platforms such as Databricks, Delta Lake, PySpark, or Unity Catalog
- Parquet / Arrow for analytical data export
- Docker and Makefile-based development workflows
- Tech stack: PostgreSQL / PostGIS, Python, rasterio / GDAL, AWS, Docker
- Recruiter screening
- Hiring manager interview
- Technical task
- Technical panel interview
- Final interview
- Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions.
- See the big picture: Stay close to what's happening across the company so you can make better decisions. Consider how your work affects others.
- Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions.
- Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift.
- Have fun: What we do matters-and it should be enjoyable. Celebrate progress, take pride in results, and share the wins.