Data Analyst

Basingstoke
3 weeks ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

We do what we say…
And we're keen to hear from people like you who make it their business to get things done!

The purpose of this role is to enable integrated, automated, and consistent programme reporting through the effective use of Microsoft Power Platform tools and structured data practices. The Digital & Data Analyst is responsible for consolidating and modelling data from multiple sources-including trackers, Microsoft Lists, and existing systems such as Sypro, P6, IFS, ACC and GIS-to support PMO governance and programme performance reporting.

The role focuses on improving data quality, creating automated workflows, and developing Power BI dashboards that reduce manual effort and provide accurate insights. Working closely with PMO functions and delivery teams, the Digital & Data Analyst helps embed digital processes, improve data literacy, and prepare the programme for future system integration and advanced data engineering.

Working alongside team colleagues and other Clancy departments or functions, your role will contribute to the following activities:

Data Collection & Quality

Collect, clean, organise, and validate programme data from trackers, Microsoft Lists, and existing systems (Sypro, P6, IFS, ACC, GIS).
Apply data quality rules and checks to ensure accuracy, consistency, and completeness.
Identify data gaps, duplication, and inconsistencies, working with functional teams to correct and prevent issues..Data Modelling & Reporting

Develop and maintain data models and reporting datasets using Power BI, Power Query, SQL, and structured schema principles.
Consolidate information from multiple sources into integrated datasets to support PMO governance, project controls, and decision-making.
Maintain reporting logic, relationships, and definitions aligned with PMO standards.Automation & Integration Support

Build automated data collection processes and workflow solutions using Power Automate and API-based data extracts.
Streamline reporting processes by reducing manual data manipulation and introducing scheduled refreshes and automations.
Support early-stage system integration activities by preparing data structures, understanding backend exports, and aligning reporting needs across Sypro, P6, IFS, ACC, and GIS..Dashboard Development

Develop and maintain Power BI dashboards aligned with PMO reporting cycles and project controls requirements.
Improve clarity, usability, and performance of dashboards based on user feedback.Tracker & List Management

Help standardise PMO trackers, including version control and data input rules.
Introduce and maintain Microsoft Lists for structured data capture where appropriate, as well as integrate other data entries from different source systems.Collaboration & Support

Work with PMO, Planning, Project Controls, Commercial, Risk, and Delivery Teams to align data and reporting.
Provide technical support for data-related queries, business logic, and integration considerations.Governance & Continuous Improvement

Ensure reporting outputs follow PMO governance standards and definitions.
Contribute to improving data processes, digital tools, and reporting practices across the programme.We'd love to hear from you, if you can demonstrate...

Training or experience in data analytics, business intelligence, or digital reporting.
Strong proficiency with Microsoft Power BI (data modelling, DAX, Power Query).
Familiarity with Microsoft 365 tools including Excel, SharePoint, and Power Automate.
Experience developing Power BI dashboards and automated reporting solutions.
Experience cleaning, structuring, and modelling data from multiple sources.
Experience using Power Query, SQL, and basic automation flows (Power Automate).
Experience working with structured trackers, Lists, or similar data capture tools.
Experience collaborating with cross-functional teams in a fast-paced environment.
Strong knowledge of Power BI: data modelling, relationships, DAX, Power Query.
Ability to work with SQL for data extraction, joins, and transformations.
Understanding of data quality, validation, and version-control principles.
Ability to standardise and govern trackers and Lists for consistent data capture.
Good understanding of workflow automation using Power Automate.
Strong analytical and problem-solving skills.
Clear communication skills, especially with non-technical stakeholders

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.

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.