Senior Data Analyst Water

Manchester
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst - HOTH, Permanent

Senior Business Data Analyst

Senior Consultant Data Analyst

Senior Performance & Data Analyst

Senior Performance & Data Analyst

Senior Cost Intelligence Data Analyst

Senior Data Analyst - Water

Salary: circa £50k 

Location: Hybrid – Manchester

Contract: Fixed Term 3 months (with potential extension to 6 months)

The Vacancy

Multitask Personnel are working with a company at the forefront of energy and utility innovation. They own and manage essential energy infrastructure assets that offer smarter energy solutions for all.

Through smart metering, installation, data services, EV charging infrastructure, and the electrification of heat, they are creating a more sustainable future. As they expand their capabilities in managing SMART water meters, we are recruiting a highly skilled Senior Data Analyst to lead the design and development of robust processes, systems, and data strategies that support operational excellence.

If you're passionate about data, thrive in dynamic environments, and want to shape the future of utilities, this is the opportunity for you.

The Role

As the Senior Data Analyst, you will play a pivotal role in driving the success of the company’s SMART water meter project. Your responsibilities will include:

•    Process Development: Defining interfaces, data transfer standards, and end-to-end processes for water meter data between multiple third parties.

•    Data Management: Ensuring data consistency, accuracy, and completeness across external parties.

•    Systems Implementation: Collaborating with IT to define system and data requirements, enabling financial and performance analysis at the asset level.

•    Analysis and Reporting: Creating dashboards, reports, and visualizations to monitor contract performance and data quality.

•    Stakeholder Engagement: Partnering with project managers, operational teams, and IT to translate business challenges into effective solutions.

Key Responsibilities

•    Develop processes to support the ownership, installation, and management of SMART water meters.

•    Lead GAP analysis to identify areas for improvement in current processes and data systems.

•    Design, implement, and monitor data validation processes to maintain data quality.

•    Document and communicate data insights to stakeholders at all levels.

•    Define customer journeys and external interfaces while maintaining GDPR compliance.

•    Support user acceptance testing, training, and smooth project transitions to BAU.

The Ideal Candidate

We are looking for someone with a proven track record in data analysis, process development, and stakeholder collaboration.

•    Background in the metering, water, or energy industries is desirable.

•    Extensive experience in data analysis for large/complex projects or programs.

•    Strong analytical and problem-solving skills, with experience in business process modelling and data analysis.

•    Ability to create comprehensive documentation such as business cases, requirements specifications, and cost/benefit analyses.

•    Proficient in Microsoft Office tools, including Excel, PowerPoint, and Visio.

•    Excellent communication and stakeholder management skills, with leadership capabilities.

•    Familiarity with Agile methodologies, UAT processes, and data security issues.

•    Understanding of the energy industry landscape.

To apply for this role, please send your CV to (url removed)

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.

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.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.