Data Engineer · 20 Farringdon

Lumonpay
London
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Data Engineer (KTP Associate Position) - Salford

Data Engineer | Remote

Data Engineer

Data Engineer

Data Engineer

Who are we?

Lumon is a leading foreign exchange and international payments company which enables effortless overseas payments by uniting people, technology & expertise. We are passionate about what we do, as we believe helping people and companies achieve their goals is more valuable than just moving their money.

Life at Lumon

People are always at the heart of what we do, no matter if it’s our clients, our community or our Lumoneer's. To give you a little flavour of what we mean…

We are a proud charity partner of Future First who support children in schools across the UK. We are a carbon neutral business. We are committed to Equity, Diversity and Inclusion; our EDI committee serves an important role at Lumon by challenging the status quo and helping us move the needle. We really want to make Lumon a great place to work, where people feel comfortable being themselves and where we can attract as much diversity as we possibly can. If this sounds good to you, then we’re thrilled you’re considering joining our team!

Role Overview

The Data Engineer will play a critical role in implementing and maintaining Lumon’s technical data infrastructure. Reporting to the Head of Data, you will focus on building and optimising the Azure Synapse data warehouse, developing robust data pipelines, and delivering high-quality datasets for reporting and analytics.
This role is ideal for a technically skilled professional who thrives on designing scalable data solutions and enjoys solving complex data challenges.

Key Responsibilities

Data Infrastructure and Pipeline Development

  • Design, develop, and maintain ETL/ELT pipelines using Azure Synapse Analytics, Data Factory and other Azure related cloud warehouse tools.
  • Optimise the Azure Synapse data warehouse for performance, scalability, and reliability.
  • Integrate data from various internal and external sources, ensuring accuracy and consistency across datasets.

Data Transformation and Quality

  • Perform data transformation tasks to ensure datasets are structured for efficient reporting and analysis.
  • Collaborate with the Head of Data to establish and monitor data quality metrics, implementing remediation processes as necessary.

Database Management

  • Assist in managing and maintaining SQL databases, optimising query performance and ensuring secure data storage in compliance with company policies.
  • Support database migrations and implement best practices for data security and retention.

Business Intelligence Support

  • Work closely with the Data Analyst and other stakeholders to develop and deploy Power BI dashboards and reports.
  • Provide technical support for troubleshooting data-related issues and ensuring continuous availability of critical reports and datasets.

Collaboration and Problem-Solving

  • Collaborate with stakeholders to understand business requirements and translate them into technical data solutions.
  • Identify opportunities to improve existing processes and automate routine tasks to enhance efficiency.

Required Experience

  • 3+ years in data engineering or similar technical roles, preferably in financial services or a related field.
  • Hands-on experience with Azure Synapse, Pipeline creations, Azure Data Factory, and Azure Data Lake Storage.
  • Strong proficiency in SQL, including database management, query optimisation, and troubleshooting.
  • Experience building and maintaining ETL pipelines for large-scale data environments.

Technical Skills

  • Expertise in cloud-based data warehouse architecture within Azure ecosystems.
  • Strong understanding of data modelling and transformation processes.
  • Familiarity with Power BI for creating and deploying visualisations.

Attributes

  • Detail-oriented with excellent problem-solving skills.
  • Collaborative mindset and ability to work effectively across teams.
  • Eager to stay updated with emerging data engineering trends and tools.

What you'll get:

  • A competitive salary
  • 25 days Annual leave, with a chance to purchase up to 5 additional days’
  • Birthday day off
  • Free monthly company lunches
  • Summer and Christmas parties, and other fun social events throughout the year
  • Access to Pirkx benefits platform which includes online GP, cashback and discounts on shopping and restaurants, gym discounts and more!
  • Medicash health scheme for you and your family
  • Cycle to work scheme
  • Quiet/Prayer room in Head Office
  • Employee Assistance Programme
  • Mental Health First Aiders
  • Pension
  • 4x Life Assurance
  • Ongoing training and development
  • Enhanced maternity leave
  • Menopause support
  • 2 additional days a year for ‘Moments that Matter

Lumon is proud to be an Equal Opportunity Employer. We are committed to equal employment opportunity regardless of race, colour, ancestry, religion, sex, sexual orientation, age, marital status, disability, or gender identity.

If you consider yourself to have a disability or require any reasonable adjustment during the recruitment process or within the workplace, please highlight this at the earliest opportunity, we will provide appropriate support to you throughout the process.

#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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.