(Senior) Geospatial Data Engineer (f/m/x) - Remote Sensing & AI Pipelines
We are looking for a Senior Geospatial Data Engineer to build the high-performance data backbone for our multitemporal, multimodal Earth observation models. While our ML Engineers focus on model architecture, you will own the full geospatial data lifecycle: discovery, ingestion, standardisation, quality assurance, and delivery of production-ready datasets that combine very high-resolution optical and Synthetic Aperture Radar (SAR) imagery.
This is a high-impact role at the intersection of Earth Observation and AI. You will be the custodian of our geospatial "data engine" — ensuring it is scalable, deterministic, and capable of handling terabytes of multi-sensor satellite data to enable semantic understanding across sensors and time. Beyond data engineering, you will play an active role in ML lifecycle management, from dataset versioning and experiment tracking through to model deployment and monitoring.
LiveEO is a young, dynamic team that thrives on big challenges and fast learning cycles—we move quickly, stay curious, and genuinely enjoy building together. We’re on a mission to break the “curse of Earth Observation”: turning incredible satellite data into reliable, actionable decisions that people can trust and use in real operations. In this role, you’ll work in a fun, high-ownership environment where ambitious technical problems (multimodal SAR/optical foundation models) meet real-world impact—and where your ideas can go from whiteboard to production in tight, collaborative iterations.
At LiveEO, you'll sit with the AI team and partner closely with downstream product teams to translate model capabilities into measurable business value and production-ready workflows. You'll also work hand-in-hand with our dedicated data annotation team to define labelling guidelines, drive feedback loops on data quality, and ensure training and evaluation datasets reflect real-world edge cases.
You will work with:
Geospatial & EO: GDAL, Rasterio, GeoPandas, QGIS, STAC, Cloud-Optimised GeoTIFF, Zarr, PostGIS
Data Orchestration & Compute: Prefect, Ray
Data Stores: PostgreSQL + PostGIS
Cloud: AWS (S3, EC2, and supporting infrastructure)
ML Lifecycle: Databricks, MLflow, PyTorch, PyTorch Lightning
Core Language: Python
Geospatial Data Discovery & Management
Design and operate scalable EO data discovery workflows integrating STAC-compliant catalogues to ingest high-value optical and SAR datasets.
Maintain structured metadata stores in PostgreSQL/PostGIS, tracking provenance, sensor parameters, and coverage gaps across all data assets.
EO Data Processing & Standardization
Build and maintain ETL/ELT workflows using Prefect and Ray to process satellite imagery at scale, including radiometric calibration, orthorectification, co-registration, and SAR pre-processing (speckle filtering, polarimetry, coherence).
Own tiling and patch-generation strategies, and develop deterministic pipeline execution frameworks that behave consistently across geographies and acquisition conditions.
Data Quality Assurance & Diagnostics
Design and automate QA checks across the full pipeline — band integrity, co-registration accuracy, label alignment, and class distribution — with monitoring to detect data drift before it reaches model training.
Own evaluation dataset curation, ensuring splits are geographically stratified, temporally balanced, and leakage-free.
ML Lifecycle & Data-Model Integration
Own dataset versioning and lineage tracking in MLflow and Databricks, and partner with ML Engineers to deliver production-ready data loaders and inference interfaces for PyTorch Lightning workflows.
Cloud Infrastructure & Tooling
Maintain our AWS-based cloud stack and Databricks environments, and drive adoption of cloud-native geospatial standards (COG, Zarr, STAC) to future-proof the data platform.
Must have:
Geospatial Engineering Expertise: Deep hands-on experience with geospatial data formats, projections, and operations. Proficiency with GDAL, Rasterio, GeoPandas, and STAC for large-scale EO data handling and standardization.
Remote Sensing Foundations: Solid understanding of satellite-based remote sensing principles for both optical and SAR sensors, including familiarity with SAR pre-processing concepts (calibration, speckle, polarimetry) and common data formats (GeoTIFF, SAFE, HDF5).
Data Quality & Pipeline Reliability: Demonstrated ability to design and automate data quality frameworks, including validation logic, anomaly detection, and monitoring for large-scale geospatial pipelines.
Strong Software Engineering: Mastery of Python with a focus on clean, maintainable, and testable code in a production environment.
Data Orchestration & Distributed Compute: Proficiency in Prefect (or Airflow) and distributed computing frameworks like Ray for large-scale processing.
Database Management: Strong knowledge of PostgreSQL/PostGIS for managing complex geospatial metadata at scale.
Pragmatic Delivery: A mindset that balances robust, long-term infrastructure with practical, iterative delivery in a fast-moving research environment.
MLOps & ML Lifecycle: Hands-on experience with MLflow, Databricks, and dataset versioning best practices; familiarity with PyTorch Lightning to support the ML R&D lifecycle.
Cloud & Big Data: Experience with AWS infrastructure and Databricks for large-scale data processing.
Advanced SAR Processing: Experience with dedicated SAR pre-processing libraries (e.g., SNAP, PyroSAR, s1tbx) and multi-temporal coherence analysis.
Cloud-Native Geospatial Standards: Practical experience with Cloud-Optimised GeoTIFF, Zarr, or STAC API implementations.
The opportunity to create a product that can improve business processes and lives across the globe.
Flexible working hours and hybrid work model - we trust our employees to get their work done while maintaining a healthy work-life balance.
We empower employees to drive their own career development, take initiative and have the freedom to be creative and bold.
Not an overtime culture - we take care that overtime is done only as a necessity and always offset with time off and rest.
A collaborative and learning environment - frequent internal workshops, knowledge sharing sessions, journal clubs and hackathons.
Office located in the centre of Berlin Kreuzberg with free fruit, nuts and drinks.
Potential to participate in the employee stock option program.
Urban Sports membership and BVG subsidy, corporate pension program.
A diverse and vibrant international environment of 30+ different nationalities.
