Finance Data Engineer

Qh4 Consulting
London
1 year ago
Applications closed

Related Jobs

View all jobs

Manager, Data Engineering

Vice President, Lead Data Engineer

Finance Data Analyst

Finance Data Analyst

Senior Data Engineer for Finance AI Pipelines

Senior Azure Data Engineer – Databricks, AI & Finance

Data Engineer – Finance Analytics Technology


We are looking for a dedicatedData Engineerto join the Finance Analytics Technology team. In this role, you’ll play a key part in building, maintaining, and optimising a modern data ecosystem. This permanent opportunity involves working with leading technologies, includingSnowflake, Python, Informatica, andAzure, to deliver high-quality data solutions that support business-critical decision-making.


With ahybrid working modeland three days a week in the office, this role provides the chance to collaborate closely with cross-functional teams in a dynamic and supportive environment.]


Key Responsibilities:


  • Design, build, and optimise scalable data pipelines usingETLandELTmethodologies.
  • UtiliseSnowflakefor efficient data storage, processing, and analytics.
  • Automate data processes and integrate data from multiple sources usingPythonandSQL.
  • LeverageAzure cloud-native technologiesto enhance data infrastructure, ensuring scalability, performance, and security.
  • Collaborate with data analysts, BI developers, and enterprise data teams to align solutions with business requirements and maintain data governance standards.
  • Apply domain knowledge in finance-related data to improve accuracy, enhance models, and meet business needs.
  • Stay informed about developments in cloud and data technologies, contributing to the organisation’s data strategy.
  • Participate fully in the agile development lifecycle, including sprint planning, design reviews, and delivering data tasks within two-week cycles.
  • Ensure compliance with existing standards while contributing to the refinement of best practices in cloud data engineering.


Essential Skills


  • Expertise in building data pipelines and architectures withSnowflake, Python, andInformatica.
  • Familiarity withAzureand other cloud-native technologies.
  • Strong understanding of finance-related data domains and their application in data engineering.
  • Problem-solving ability, combined with excellent collaboration and communication skills, to work effectively with technical and non-technical teams.
  • Experience working within modern technology stacks and agile methodologies.
  • Background in collaborating with geographically distributed development teams.


Desirable Skills

  • Knowledge of reporting tools such asPower BI.
  • Familiarity withSAP FI datasetsor platforms likeSAP BW, SAP Analysis, andBusiness Objects.


This is an exciting opportunity to contribute to meaningful data-driven initiatives, working with a forward-thinking team on innovative projects. If this sounds like your next step, we’d love to hear from you!

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.