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- Tasks: Build and maintain geospatial data pipelines and APIs using cutting-edge technologies.
- Company: Join nxzen, a rapidly growing company redefining location intelligence.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with strong focus on continuous improvement and learning.
- Why this job: Make a real impact in the geospatial field while working with innovative tech.
- Qualifications: Experience with Python, SQL, and geospatial data processing is essential.
The predicted salary is between 40000 - 50000 ÂŁ per year.
Joining nxzen’s rapidly growing geospatial capability means becoming part of a team redefining how organisations use location intelligence across critical infrastructure sectors. As a Geospatial Data Engineer, you will play a key role in building reliable spatial data pipelines, automated workflows, APIs, and transformation processes that underpin our next generation of geospatial solutions. Reporting to the UK GIS Lead, you will support day‑to‑day delivery across data‑engineering workstreams, contribute to solution implementation, and help ensure the smooth operation of our geospatial data workflows. This role combines hands‑on engineering with opportunities to influence technical approaches, helping to improve the reliability, automation, and performance of our pipelines.
You will build ETL processes, transform and validate geospatial datasets, support API and service implementations, contribute to CI/CD workflows, and enhance observability across our geospatial data operations.
The role reports to the UK GIS Lead and will be responsible for:
- Developing and maintaining end‑to‑end spatial ETL pipelines in collaboration with consultants, data engineers, architects, and platform teams.
- Implementing geospatial data services and APIs that reliably expose processed datasets for analytical, operational, and mapping use cases.
- Optimising query performance, indexing strategies, and storage patterns across spatial databases and cloud‑hosted datasets.
- Contributing to improvements in CI/CD processes that automate testing, validation, and deployment of geospatial data workflows.
- Enhancing observability through structured logging, data‑quality checks, lineage tracking, and pipeline health indicators.
Key Responsibilities
- Data Pipeline Delivery: Build and maintain geospatial ETL pipelines using Python, SQL, FME, ArcGIS tools, or similar technologies. Prepare, transform, validate, and load spatial datasets from multiple structured and unstructured sources. Implement workflow scheduling, automation, and repeatable processing patterns. Diagnose issues, contribute to root‑cause analysis, and implement stabilisation fixes. Support performance optimisation including indexing, partitioning, caching, and schema refinement.
- Data Services & API Implementation: Implement geospatial APIs and services to publish processed datasets. Configure secure access patterns, schema‑aware endpoints, and version‑controlled data outputs. Support integrations with upstream and downstream systems.
- Solution Implementation: Contribute to geospatial data‑engineering components of larger solutions. Configure data storage, ETL environments, and processing layers. Assist with deployment scripts, infrastructure configurations, and environment setup.
- Data & Analysis: Support data preparation, transformation, and migration activities using tools like ArcGIS Pro, FME, Python, or ModelBuilder. Develop repeatable processes for data quality, validation, and structured workflows.
- Observability, Quality & Reliability: Add structured logging, validation checks, and lineage tracking into pipelines. Contribute to dashboards monitoring pipeline health, reliability, and data‑quality metrics. Apply testing and engineering discipline to improve predictability and reduce defects.
- Documentation & Governance: Produce clear documentation describing pipeline behaviour, data flows, dependencies, and operational expectations. Maintain structured coding practices, version control discipline, and consistent naming and practices.
- Practice Support & Collaboration: Work closely with peers and senior engineers to adopt best‑practice engineering patterns. Participate in peer reviews and knowledge‑sharing sessions. Provide input into continuous improvement of internal engineering methods and processes.
Essential Skills & Experience:
- Competent with Python, SQL, PostgreSQL, and PostGIS to develop, optimise, and operate reliable geospatial data workflows.
- Strong hands‑on experience with geospatial data processing, ETL workflows, and spatial transformations.
- Practical expertise with geospatial data formats (e.g., Shapefile, GPKG, GeoJSON, FGDB, GeoTIFF, GeoParquet etc.), coordinate systems, and spatial data standards.
- Experience with cloud platforms (Azure, AWS, or GCP) for data engineering workloads.
- Experience using ESRI tools, FME, GDAL/OGR or equivalent technologies.
- Understanding of spatial indexing, query optimisation, schema design, and data‑model evolution.
- Experience building repeatable workflows for data validation, quality checks, logging, and structured processing.
- Ability to communicate technical details clearly and collaborate effectively across multidisciplinary teams.
- Strong problem‑solving skills and a methodical approach to debugging and optimisation.
Desirable (but not essential) Skills & Experience:
- Experience in utilities, infrastructure, or other asset‑intensive sectors.
- Experience with ArcGIS Utility Network is highly desirable.
- Familiarity with CI/CD pipelines, DevOps practices, and infrastructure‑as‑code.
- Exposure to APIs, microservices, or event‑driven data architectures.
- Knowledge of ArcGIS Enterprise, ArcGIS Online, or other geospatial platforms.
- Scripting or workflow automation experience beyond core data engineering.
- Exposure to open‑source geospatial tools and libraries.
Geospatial Engineer employer: nxzen Global
Contact Detail:
nxzen Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Geospatial Engineer
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the geospatial field. Attend meetups, webinars, or even local events. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving ETL pipelines or geospatial data processing. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Don’t just apply blindly! Tailor your approach for each role. Research nxzen and their projects, then highlight how your experience with Python, SQL, and geospatial tools aligns with their needs. A personal touch goes a long way!
✨Tip Number 4
Keep it real during interviews! Be ready to discuss your hands-on experience with geospatial data workflows and problem-solving skills. Show them you’re not just about theory but can tackle real-world challenges head-on.
We think you need these skills to ace Geospatial Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Geospatial Engineer role. Highlight your experience with Python, SQL, and any geospatial tools you've used. We want to see how your skills match what we're looking for!
Showcase Your Projects: Include specific projects where you've built ETL pipelines or worked with geospatial data. This gives us a clear picture of your hands-on experience and how you tackle real-world problems.
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points for key achievements and avoid jargon unless it's relevant. We appreciate straightforward communication!
Apply Through Our Website: Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at nxzen Global
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, SQL, and PostGIS. Brush up on your experience with geospatial data formats and tools like ArcGIS and FME, as these will likely come up during technical discussions.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles related to geospatial data workflows. Be ready to explain how you diagnosed issues and implemented solutions, especially around performance optimisation and ETL processes.
✨Demonstrate Collaboration
This role involves working closely with various teams, so be prepared to share examples of how you’ve successfully collaborated in the past. Highlight any experiences where you contributed to peer reviews or knowledge-sharing sessions, as this shows you value teamwork.
✨Ask Insightful Questions
At the end of the interview, don’t forget to ask questions that show your interest in the company’s projects and future directions. Inquire about their current geospatial initiatives or how they approach CI/CD processes, which can demonstrate your enthusiasm for the role.