Finance Data Engineer

Qh4 Consulting
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer - Fabric - £70,000 - London

Data Engineering Consultant

Big data/ETL /SQL Data Engineer Dublin contract

Data Architect - Contract

Data Analyst

Big Data Lead

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!

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.