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
2 weeks ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

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.

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 Get a Better Data Science Job After a Lay-Off or Redundancy

Redundancy can be tough to face, especially in a competitive field like data science. But it’s important to know: your experience, analytical thinking, and modelling skills are still in demand. Across sectors like healthcare, finance, e-commerce, government and AI startups, UK employers continue to seek data scientists who can deliver value through insight, prediction, and automation. This guide will walk you through how to bounce back from redundancy with purpose and clarity—whether you're a data analyst looking to step up, a mid-level data scientist, or a machine learning specialist seeking a better-aligned opportunity.

Data Science Jobs Salary Calculator 2025: Find Out What You Should Earn in the UK

Why last year’s pay survey is already out of date for UK data scientists “Am I being paid enough?” Every data professional eventually asks that question—often after a teammate reveals a hefty counter‑offer, a recruiter shares a six‑figure opening, or a headline trumpets the latest multimillion‑pound AI investment. Yet salary guides published even twelve months ago belong in a museum. Generative‑AI hype re‑priced Machine‑Learning Engineer roles, LLM fine‑tuning turned Prompt Engineering into a genuine career path, & fresh regulation forced companies to hire Responsible‑AI Officers on senior‑scientist money. To cut through the noise, DataScience‑Jobs.co.uk distilled a transparent, three‑factor formula. Insert your role, your region, & your seniority, and you’ll get a realistic 2025 salary benchmark—no stale averages, no vague ranges. This article walks you through the formula, examines the forces pushing data‑science pay ever higher, and offers five concrete actions to boost your market value within ninety days.

How to Present Data Science Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

The ability to communicate clearly is now just as important as knowing how to build a predictive model or fine-tune a neural network. In fact, many UK data science job interviews are now designed to test your ability to explain your work to non-technical audiences—not just your technical competence. Whether you’re applying for your first data science role or moving into a lead or consultancy position, this guide will show you how to structure your presentation, simplify technical content, design effective visuals, and confidently answer stakeholder questions.