ECB Data Analyst

London
3 days ago
Create job alert

ECB Data Analyst* (Contract)

Duration: 6 Months (Possibility for extension)

Location: London/Hybrid (3 days per week on site)

Rate: A highly competitive Umbrella Day Rate is available for suitable candidates

Role Profile

The External Data Analyst will support the ECB Onboarding Programme by translating business and regulatory requirements into clear external data needs and mapping these to existing datasets, systems, and vendor sources.

The role will work closely with business stakeholders, Technology, Data Management, Legal, and Procurement to identify where current external data provision does not meet requirements, assess vendor and dataset gaps, and drive the delivery of remediation actions or new data sourcing.

The analyst will ensure that all required data is accurately identified, traceable, contractually compliant, and available to regulatory submissions, controls, and programme milestones.

Key Accountabilities:

Translate business and regulatory requirements into external data specifications, mapping them to existing and new datasets, systems, and vendor sources to identify gaps.
Work collaboratively with Data and Technology teams to define ingestion, integration, storage, and metadata needs for external data supporting ECB onboarding.
Assess the suitability and coverage of current vendor datasets, identifying data availability, completeness, and quality gaps that may impact ECB reporting or controls.
Work with Data Quality and Data Governance teams to validate data standards, lineage, definitions, and controls, ensuring alignment with ECB expectations.
Ensure all ingestion flows and architectural designs comply with vendor licensing, including usage rights, redistribution restrictions, and entitlement rules.
Coordinate with Legal, Procurement to validate licensing requirements, address contractual gaps, and support sourcing of additional datasets when needed.
Document source‑to‑target mappings, lineage, licensing rules, and ingestion patterns to support ECB traceability, governance artefacts, and internal audit readiness.
Track and deliver remediation actions for data, architectural, quality, or licensing gaps, providing clear reporting of risks, issues, and dependencies to ECB programme governance.

Skills & Experience:

Experience translating regulatory or business requirements into clear data specifications and mappings.
Strong understanding of external data from key financial vendors (Bloomberg, Refinitiv, S&P, Moody's, Fitch).
Proven ability to work with Data and Technology teams to understand requirements for ingestion, integration, and system flows.
Knowledge of data licensing, usage rights, entitlement models, and redistribution constraints.
Experience running data sourcing exercises - identifying and evaluating vendors.
Experience collaborating with Data Governance teams on lineage, metadata, controls, and standards.
Strong documentation skills, including mapping, lineage, and technical requirement artefacts.
Effective stakeholder management, working with Technology, Business SMEs, Legal, Procurement
Experience with regulatory onboarding programmes or data remediation.
Familiarity with data governance frameworks (e.g., BCBS 239, EDM Council standards).
Exposure to vendor contract review, sourcing processes, and commercial/licensing negotiations.
Awareness of cloud data architecture concepts and licensing implications (Azure, AWS).
Experience with data quality tooling or profiling methods.
Technical literacy such as basic ability to interpret vendor data schemas.
Knowledge of ESG specific external data sources

Candidates will need to show evidence of the above in their CV in order to be considered.

If you feel you have the skills and experience and want to hear more about this role 'apply now' to declare your interest in this opportunity with our client. Your application will be observed by our dedicated team.

We will respond to all successful applicants ASAP however, please be advised that we will always look to contact you further from this time should we need further applicants or if other opportunities arise relevant to your skillset.

Pontoon is an employment consultancy. We put expertise, energy, and enthusiasm into improving everyone's chance of being part of the workplace. We respect and appreciate people of all ethnicities, generations, religious beliefs, sexual orientations, gender identities, and more. We do this by showcasing their talents, skills, and unique experience in an inclusive environment that helps them thrive.

As part of our standard hiring process to manage risk, please note background screening checks will be conducted on all hires before commencing employment.

We use generative AI tools to support our candidate screening process. This helps us ensure a fair, consistent, and efficient experience for all applicants. Rest assured, all final decisions are made by our hiring team, and your application will be reviewed with care and attention

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.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

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.