Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Revenue Data Analyst

Paymentology
London
3 weeks ago
Applications closed

Related Jobs

View all jobs

Financial Data Analyst - London Heathrow

FinOps Data Analyst

FinOps Data Analyst

FinOps Data Analyst

Data Analyst

Financial Data Analyst

Overview

Join to apply for the Revenue Data Analyst role at Paymentology.

At Paymentology, we’re redefining what’s possible in the payments space. As the first truly global issuer-processor, we give banks and fintechs the technology and talent to launch and manage Mastercard, Visa, and UnionPay cards at scale across more than 60 countries. Our advanced, multi-cloud platform delivers real-time data, unmatched scalability, and the flexibility of shared or dedicated processing instances. It’s this global reach and innovation that sets us apart.

We’re looking for a Revenue Data Analyst to deliver actionable insights that drive commercial performance, pipeline health, and revenue optimisation. Leveraging advanced analytics, CRM data, and BI tools, this role supports strategic decision-making across commercial, finance, product, and executive teams. It blends technical data manipulation with strong business acumen to influence go-to-market execution and growth planning.

What you get to do
  • You will analyse revenue, sales, and client performance to surface trends, risks, and opportunities influencing go-to-market strategy, pricing, and expansion.
  • You will build, automate, and maintain dashboards and KPI reports used by commercial, finance, and leadership teams.
  • You will partner with cross-functional teams across Revenue, Product, Tech, and Finance to ensure data integrity, accurate forecasting, and timely reporting.
  • You will own the cadence of revenue KPI reporting, ensuring consistent, high-quality output for executive stakeholders.
  • You will enhance CRM data models to improve pipeline hygiene, forecasting accuracy, and performance tracking.
  • You will conduct deep-dive ad hoc analyses on topics like pricing strategy, market expansion, partner performance, and customer segmentation.
  • You will identify and implement automation opportunities that reduce manual reporting effort and improve data delivery speed.
What it takes to succeed
  • You bring 3–5 years of experience in data analysis, revenue operations, or BI roles, preferably in fintech, SaaS, or payments.
  • You have advanced Excel modelling skills and, ideally, SQL knowledge.
  • You have experience in CRM reporting (Salesforce, HubSpot) with proven improvements in data hygiene and forecasting.
  • You are proficient in BI tools such as Power BI or Tableau for dashboard creation and data visualisation.
  • You have the ability to communicate insights effectively to non-technical stakeholders.
  • You thrive in fast-paced, high-growth, and ambiguous environments.
Education & Experience
  • You have a background in data analysis, business intelligence, or revenue operations, with strong technical and commercial acumen.
What you can look forward to

At Paymentology, it’s not just about building great payment technology, it’s about building a company where people feel they belong and their work matters. You’ll be part of a diverse, global team that’s genuinely committed to making a positive impact through what we do. Whether you’re working across time zones or getting involved in initiatives that support local communities, you’ll find real purpose in your work - and the freedom to grow in a supportive, forward-thinking environment.


#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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.