Geospatial Data Engineer

Geospatial Data Engineer – Glasgow or London

About this role:

We are looking for a Geospatial Data Engineer to join our team. We are growing our Data and Services business, creating insights from satellites developed and operated in-house.

You will design, operate, and improve our ArcGIS platform and data products that power apps, dashboards, and decision-ready analytics across our maritime and forestry programs. This role blends hands-on development in ArcGIS Online (AGOL) and ArcGIS Pro with Remote Sensing, Machine Learning (ML) on satellite imagery, and modern Dev Ops (Continuous Integration/Continuous Delivery (CI/CD) and Infrastructure as Code (IaC)) to deliver reliable, secure, and cost-effective services on ArcGIS Online (AGOL) and Amazon Web Services (AWS).

This opportunity is ideal for someone with a passion for product quality and innovation, who has experience of working with Earth Observation and geospatial data. You should be comfortable working in an Agile development environment. The position offers the opportunity to share your expertise in a fast-growing space technology company that is bringing new data and insights to address challenges in business and government.

The role is based in either Glasgow or London where Hybrid Working is available. The nature of the role will require essential, regular business travel (both local and global) plus the occasional ‘out of hours’ work that may be required to support customer and business activities.

Please note that in future, you may be required to complete a Basic Disclosure check (https://(Use the "Apply for this Job" box below).)

Key Responsibilities:

• ArcGIS platform administration – Support administration of ArcGIS Online (AGOL) and ArcGIS Pro: configure users, groups, permissions, content life cycles, sharing, and security.
• Applications and dashboards – Build and maintain web apps (Experience Builder/Instant Apps) and ArcGIS Dashboards to present Automatic Identification System (AIS) and Earth Observation (EO) insights.
• Remote sensing and image analysis – Use ArcGIS Pro and Python (e.g., Arc Py, rasterio, geopandas) with Machine Learning for pre-processing, feature extraction, classification/segmentation, and accuracy checks.
• Data engineering and pipelines (AWS + Python) – Build Extract-Transform-Load (ETL) pipelines for AIS/imagery using S3 and scheduled processing (Lambda/Batch); validate, add metadata, and publish to AGOL/feature services with versioned schemas.
• User experience (UX) for maps/dashboards – Create clear symbology, filters, accessibility basics (colour/contrast) for web layers, web maps & web apps.
• Dev Ops ways of working – Implement Continuous Integration/Continuous Delivery (CI/CD), containerize with Docker, and use Infrastructure as Code (IaC) (Terraform/Cloud Formation) with sensible logging, metrics, and alerts.
• Agile and Waterfall delivery – Work in sprints (backlog, estimates, reviews) or stage-gated phases (requirements to release); keep documentation and acceptance criteria current.
• Domain-driven analysis (forestry/environmental engineering or maritime) – Apply domain experience to frame problems, choose appropriate analytical & development methods, validate outputs, and explain results plainly.
• Data governance, licensing, and security – Follow naming/metadata/lineage standards.
• Stakeholder engagement and enablement – Work with the Team Leader, analysts, and partners to refine needs, demo progress, capture feedback, and produce concise runbooks/how-tos.
• Cost, performance, and reliability management – Track AWS/Esri usage and spend; optimise storage/compute/sharing; meet service targets and resolve incidents with root-cause actions to keep services sustainable.
• Continuous Improvement – Proactively improving how we build and run our geospatial analytical & visualisation services.

Skills required:

• Significant domain practice of forestry, environmental engineering, or maritime, applying ArcGIS and Python to real problems.
• Python for geospatial & imagery – Arc Py, rasterio, geopandas;
  Machine Learning (ML) exposure (scikit-learn, PyTorch or Tensor Flow).
• Dev Ops on cloud – Continuous Integration/Continuous Delivery (CI/CD),…