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

Nominate & Attend

Global Data Engineer

epay, a Euronet Worldwide Company
Billericay
4 weeks ago
Create job alert

Responsibilities

of the role:
Data Pipeline Development:Build and maintain batch and streaming pipelines using Azure Data Factory and Azure Databricks.Data Categorisation & Enrichment:Structure unprocessed datasets through tagging, standardisation, and feature engineering.Automation & Scripting:Use Python to automate ingestion, transformation, and validation processes.ML Readiness:Work closely with data scientists to shape training datasets, applying sound feature selection techniques.Data Validation & Quality Assurance:Ensure accuracy and consistency across data pipelines with structured QA checks.Collaboration:Partner with analysts, product teams, and engineering stakeholders to deliver usable and trusted data products.Documentation & Stewardship:Document processes clearly and contribute to internal knowledge sharing and dataernance.Platform Scaling:Monitor and tune infrastructure for cost-efficiency, performance, and reliability as data volumes grow.On-Call support: Participate in an on-call rota system to provide support for the production environment, ensuring timely resolution of incidents and maintaining system stability outside of standard working hours.
Requirements

What you will need:

The ideal candidate will be proactive and willing to develop and implement innovative solutions, capable of the following:

Rmended:
2+ years of professional experience in a data engineering or similar role. Proficiency inPython, including use of libraries for data processing (, pandas, pySpark). Experience working withAzure-based data services, particularlyAzure Databricks, Data Factory, and Blob Storage. Demonstrable knowledge of data pipeline orchestration and optimisation. Understanding of SQL for data extraction and transformation. Familiarity with source control, deployment workflows, and working in Agile teams. Strongmunication and documentation skills, including translating technical work to non-technical stakeholders.
Preferred:
Exposure to machine learning workflows or model preparation tasks. Experience working in a financial, payments, or regulated data environment. Understanding of monitoring tools and logging best practices (, Azure Monitor, Log Analytics). Awareness of cost optimisation and scalable design patterns in the cloud. Job ID 788405236A

Related Jobs

View all jobs

Azure Data Engineer

Senior Data Analyst - Pricing Data Engineering & Automation, CUO Global Pricing

Senior Data Analyst - Pricing Data Engineering & Automation, CUO Global Pricing

Pricing & Revenue Data Scientist

Senior Data Analyst

Senior Data Engineer | Global Trading Technology Firm

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 Machine Learning Job After a Lay-Off or Redundancy

Redundancy in machine learning can feel especially frustrating when your role was technically advanced, strategically important, or AI-facing. But the UK still has strong demand for machine learning professionals across fintech, healthtech, retail, cybersecurity, autonomous systems, and generative AI. Whether you're a research-oriented ML engineer, production-focused MLOps developer, or applied scientist, this guide is designed to help you bounce back from redundancy and find a better opportunity that suits your goals.

Machine Learning Jobs Salary Calculator 2025: Figure Out Your True Worth in Seconds

Why last year’s pay survey is useless for UK ML professionals today Ask a Machine Learning Engineer wrangling transformer checkpoints, an MLOps Lead firefighting drift alarms, or a Research Scientist training diffusion models at 3 a.m.: “Am I earning what I deserve?” The honest answer changes monthly. A single OpenAI model drop doubles GPU demand, healthcare regulators release fresh explainability guidance, & a fintech unicorn pays six figures for vector‑search expertise. Each shock nudges salary bands. Any PDF salary guide printed in 2024 now looks like an outdated Jupyter notebook—missing the gen‑AI tsunami, the surge in edge inference, & the UK’s new Responsible‑AI framework. To give ML professionals an accurate benchmark, MachineLearningJobs.co.uk distilled a transparent, three‑factor formula that estimates a realistic 2025 salary in under a minute. Feed in your discipline, UK region, & seniority; you’ll receive a defensible figure—no stale averages, no guesswork. This article unpacks the formula, highlights the forces driving ML pay skyward, & offers five practical moves to boost your value inside the next ninety days.

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.