Business Data Analyst

Pontoon Solutions
1 month ago
Applications closed

Related Jobs

View all jobs

Business Data Analyst

Business & Data Analyst

Business Data Analyst

Business Data Analyst BELFAST £600/day Banking

Business Data Analyst

Data Analyst - Graduate

£650 UMB

6 month contract

Hybrid - 2 days onsite either in: Manchester, Leeds, Cmyru, Yorkshire, Halifax or Edinburgh.


We're recruiting on behalf of a leading UK bank for a Data Governance Business Analyst to support a major data retention and backup transformation programme. This role is key in shaping the bank's long-term retention (LTR) strategy, ensuring compliance with regulatory mandates (GDPR, FCA, PRA) while modernising legacy backup systems.


Project: Implement anew strategic backup solutionacross the bank to replace existing legacy infrastructure in preparation for data centre migration. The Customer Journey Managers (CJMs) will need to analyse data structure, linage, and dependencies within these systems.


Core Responsibilities

1. Data Analysis & Governance

Data Mapping– Identify, document, and classify data sources acrosslegacy and new systems.

Data Lineage Analysis– Track data movement from source to storage to ensure proper governance.

Data Dictionary Development– Standardise naming conventions, attributes, and metadata across systems.

Data Cleansing & Transformation– Identify inconsistencies in data structures and propose remediation strategies.


2. Requirement Gathering & Process Documentation

Stakeholder Workshops– Engage with business units, regulatory teams, and IT to define retention requirements.

Gap Analysis– Compare current backup retention policies with regulatory obligations and target system capabilities.

Process Modelling– Document how data flows through the bank’s systems and how long it must be retained.

Retention & Disposal Policies– Work with legal and compliance teams to define policies for secure long-term storage and timely data disposal.


3. Technology & Systems Involvement

New Platform (Dell Data Protection Suite)

• Define how data will bemigratedand structured within the new backup environment.

• Work with engineers to ensure compliance withsecurity, encryption, and access control policies.

• Help establish automation workflows forretention, archival, and disposal.

Legacy Backup Systems

• Analyse existing backup infrastructure and data storage mechanisms.

• Determine how legacy data will be transitioned without compliance risks.

• Identifydependencies between applications and legacy data storesto avoid business disruption.

Cloud & Hybrid Storage Considerations

• Work withCloud Ops & IT Security teamsto definesecure data transferstrategies.

• Align long-term retention policies withmulti-cloud and on-prem storage environments.

• Ensureauditability and access loggingfor retained data.


4. Regulatory & Compliance Alignment

Cross-Business Data Retention Standards– Ensure data meets different retention rules across banking functions (e.g., financial transactions, customer records, internal documentation).

Risk & Compliance Coordination– Align retention strategies withGDPR, FCA, PRA, and internal data governance frameworks.

Data Expiry & Secure Disposal– Establish automated policies for secure deletion of data past its retention period.


Ideal Candidate Profile – Key Skills & Experience

1. Business Analysis & Data Governance

✔ Strong experience indata analysis, data governance, and regulatory compliance.

✔ Ability tomap and document data lineageand understand how data moves across systems.

✔ Experience working in adata-heavy environmentwith structured/unstructured data.

Process modelling experience– capable of defining workflows and documenting retention policies.


2. Technical Knowledge

✔ Familiarity withbackup & recovery solutions, preferablyDell Data Protection Suite.

✔ Understanding oflegacy backup solutionsand challenges in migrating data to modern platforms.

✔ Experience withstructured (SQL, relational DBs) and unstructured (file systems, cloud storage) data management.

✔ Awareness ofcloud backup strategiesand hybrid storage models.


3. Stakeholder & Compliance Experience

✔ Experiencecollaborating with regulatory, risk, and compliance teams.

✔ Strong stakeholder engagement skills – ability to communicate technical findings to business users.

✔ Understanding ofdata retention policies and information security best practices.


4. Problem-Solving in an Ambiguous Environment

✔ Ability todefine clarity in complex, poorly documented data environments.

✔ Self-sufficient and proactive – capable of identifying issues and working towards solutions.

✔ Flexible in approach – comfortable navigating a changing organisational structure.




Pontoon Solutions 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.

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.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.