Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer

Vortexa
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Vortexa – Data Engineer

Vortexa is a fast‑growing international technology company solving the information gap in the energy industry by using massive satellite data and AI to provide a real‑time view of global seaborne energy flows.


Role Overview

You will design, build, and maintain the data production pipeline that powers Vortexa’s core SaaS platform. This involves ingesting terabytes of heterogeneous data, training and running complex AI models, and serving predictions to customers worldwide.


Key Responsibilities

  • Build and operate distributed, scalable data processing pipelines using AWS, Kubernetes, and Airflow.
  • Integrate raw satellite data with text and market data to generate high‑value forecasts (destination, cargo, vessel traffic, congestion, prices, etc.).
  • Automate data ingestion, feature engineering, model training, and deployment, ensuring 100 % uptime and fault‑tolerance.
  • Collaborate closely with data scientists, software engineers, and market analysts to translate research into production‑ready solutions.
  • Implement observability – logging, monitoring, and tracing – and improve pipeline performance and reliability.
  • Coach and mentor junior team members, fostering a culture of continuous learning and technical excellence.

Required Qualifications

  • Experience building scalable backend pipelines that process terabytes of data daily.
  • Strong software engineering fundamentals; fluency in Java and Python (knowledge of Rust is a plus).
  • Hands‑on with data lake technologies (Athena, S3), big‑data formats (Parquet, ORC, HDF5), and distributed storage.
  • Deep understanding of the full SDLC – design, code, review, test, deploy, and operations.

Nice to Have

  • Experience with Apache Kafka, Flink, or similar streaming platforms.
  • Background in web scraping and information extraction.
  • Observability expertise: logging, monitoring, tracing.
  • Knowledge of cloud native tools and infrastructure as code.

Benefits & Culture

  • Equity options granted to all staff.
  • Private health insurance via Vitality.
  • Global volunteering programme.
  • Flexible hybrid work: remote and in‑office options.
  • Tech���centric, fast‑moving environment that encourages ownership and experimentation.

Seniority & Employment

  • Mid‑Senior level
  • Full‑time
  • Location: London, England, United Kingdom (remote options)


#J-18808-Ljbffr

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.

Jobs

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Jobs

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Jobs

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.