Geospatial Data Engineer

Omnis Partners
Birmingham
10 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist - Inside IR35 - Hybrid

Data Engineer (18 Months FTC)

ArcGIS Data Analyst - Geospatial Data

Data Quality Manager

Associate Director of Geospatial Data Engineering | £140k | Remote (UK) | Open Source + Geospatial + AI-adjacent


We’re working with a forward-thinking data consultancy that’s looking for ahands-on Associate Director of Geospatial Data Engineering— someone who blends deep technical ability with creativity, and loves solving unusual data challenges with open-source tools.

This is a unique opportunity to take ownership of strategic data engineering projects — helping clients build modern, scalable data platforms while leading from the front with your own engineering expertise.


What you'll be working with:

  • Python-first data engineering, building custom pipelines, automation tools and ingestion workflows.
  • PostgreSQL + PostGIS: serious spatial querying, indexing and geospatial wrangling.
  • Cloud-native infrastructure: AWS / GCP / Azure (your pick) — but with a proper understanding of how it all works under the hood.
  • Modern ETL/ELTusing Airflow, Spark, Dagster, Prefect — you choose the right tool for the job.
  • CI/CD for data: Git, Docker, automated testing, efficient and scalable workflows.


️ What sets this role apart:

  • Heavy emphasis ongeospatial data engineering— working with OSM, map tile rendering, Leaflet.js, Kepler.gl, and data visualisation.
  • Work on projects blendingprivate + public + non-traditional data sources— edge devices, IoT, NLP pipelines, and custom data processing challenges.
  • Preference for open-source tools (QGIS, SpaCy, MapTiler etc.) over proprietary systems.
  • A chance to shape the strategy, lead delivery, and stay close to the tech.


You might be a fit if you:

  • Have a deep understanding of systems, efficiency, and optimisation (e.g., edge computing, AI-on-device, or automation with Raspberry Pi).
  • Are a natural problem-solver who can connect data points others miss.
  • Enjoy tackling messy, unconventional datasets.
  • Have a track record ofpersonal projects, open-source contributions or building beyond the 9–5

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.