National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Senior Data Engineer

Quantum Technology Solutions Inc
London
6 days ago
Create job alert

About Quantum:

Quantum is building next-generation AI and trading technologies that harness cutting-edge research and data science. As part of our rapid growth, we are seeking a highly skilled Senior Data Engineer to support our Data & AI team by designing and maintaining robust, scalable, and production-grade data systems. This is greenfield project, allowing for full product ownership and key decision making.


Role Overview:

As a Senior Data Engineer at Quantum, you will be instrumental in building the infrastructure that powers our Data Science, AI and Trading tools. You will work closely with the Data and Technology teams to ensure data accessibility, quality, and scalability - particularly focusing on trading (time-series databases), analytical, and AI pipelines.

This is a highly collaborative role, ideal for someone who thrives on taking full ownership from systems design to implementation in a fast-paced, research-driven environment and wants to be part of building world-class trading system capabilities from the ground up.


Key Responsibilities:


  • Develop and Maintain Data Pipelines:


Design, build, and optimise scalable data pipelines to support AI research and production systems, particularly for unstructured, text-heavy and time-series based datasets.


  • Data Infrastructure Design:

Architect and implement data ingestion, transformation, storage, and retrieval systems, ensuring they are resilient, high-performing, and fit for future growth.


  • Data Quality and Exploration:

Support data exploration efforts by ensuring high data quality, developing validation frameworks, and contributing to continuous data improvement initiatives.


  • Collaboration with AI Teams:

Work closely with the Principal Data Scientist to operationalise RAG systems, fine-tune data retrieval processes, and optimise training datasets for AI model development.


  • Automation and Optimisation:

Automate ETL (Extract, Transform, Load) processes, reduce manual intervention, and continuously identify opportunities to enhance the efficiency and reliability of data workflows.


  • Support Research and Prototyping:

Build and maintain flexible data systems to support rapid experimentation, research validation, and the transition of prototypes into production environments.


  • Monitoring and Troubleshooting:

Implement robust monitoring, logging, and alerting for data pipelines to proactively detect issues and maintain high availability and performance.


  • Documentation and Best Practices:

Establish and maintain high standards for data engineering documentation, coding practices, and data governance.


Required Skills and Qualifications

  • 5+ years of experience in Data Engineering, with a strong background in building data pipelines at scale.
  • Proficiency with modern data technologies (e.g Apache Airflow, Spark, Kafka, Snowflake, or similar).
  • Strong SQL skills and experience with cloud databases and data warehouses (AWS, GCP, or Azure ecosystems).
  • Expertise in working with unstructured data and NLP-related datasets.
  • Proficiency in one programming language, preferably Python with experience in data processing libraries such as Pandas, PySpark, or Dask.
  • Familiarity with MLOps and deploying AI/ML models into production environments.
  • Knowledge of Retrieval-Augmented Generation (RAG) frameworks or interest in learning and supporting RAG systems.
  • Experience implementing scalable APIs and integrating data services with AI and analytics platforms.
  • Strong understanding of data security, compliance, and governance best practices.
  • Excellent collaboration and communication skills, able to work closely with technical and non-technical stakeholders.


Preferred Qualifications

  • Experience supporting AI/ML research teams.
  • Familiarity with LLM (Large Language Model) pipelines and vector databases (e.g. Pinecone, FAISS).
  • Background in data versioning and experiment tracking (e.g DVC, MLflow).
  • Familiarity with time-series datasets and databases.


Why Join Quantum?

  • Work at the forefront of AI innovation with a team passionate about changing the future of trading and technology.
  • Take ownership and make a direct impact from day one.
  • Collaborate closely with world-class AI researchers, data scientists, and engineers.
  • Opportunity for career growth as part of a rapidly expanding AI and data science team.


Quantum is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Azure - Leeds

Senior Data Engineer - Snowflake - £100,000 - London - Hybrid

National AI Awards 2025

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 to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.

Machine Learning Jobs UK 2025: 50 Companies Hiring Now

Bookmark this page—we refresh the Hotlist every quarter so you always know who’s really scaling their ML teams. The UK’s National AI Strategy, a £2 billion GenAI accelerator fund and a record flow of private capital have kicked ML hiring into overdrive for 2025. Whether you build production‑grade LLM services or optimise on‑device models for edge hardware, employers need your skills now. Below you’ll find 50 organisations that advertised UK‑based machine‑learning vacancies or announced head‑count growth during the past eight weeks. They’re grouped into five quick‑scan categories so you can jump straight to the type of employer—and mission—that excites you. For each company we list: Main UK hub Example live or recent vacancy Why it’s worth a look (stack, impact, culture) Search any employer on MachineLearningJobs.co.uk to see real‑time adverts, or set a free alert so fresh openings drop straight in your inbox.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.