Data Analyst & Planner

Experis
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer - Burton-On-Trent

Data Analyst & Property Records Officer

Data Analyst & Property Records Officer

Data Analyst (Energy Analyst) - Contract - 2611

PPM and Data Analyst

Performance & Risk Data Analyst - Reading, Berkshire

Location: England Job Type: Contract Industry: Digital Workspace Job reference: BBBH390626_1734705386 Posted: 26 minutes ago

Role Title: Data Analyst & Planner
Duration: 6 Months
Location: Remote
Rate: £400 per day - Umbrella only

Would you like to join a global leader in consulting, technology services and digital transformation?

Our client is at the forefront of innovation to address the entire breadth of opportunities in the evolving world of cloud, digital and platforms.

Role purpose / summary

Gather and consolidate data from various sources, including application databases, user logs, and performance monitoring tools.
Ensure data integrity and accuracy, applying necessary validation and cleansing techniques to prepare data for analysis.
Application Performance Analysis:

Analyze data to assess application performance, identifying usage trends, peak loads, and potential bottlenecks.
Monitor application KPIs and metrics to evaluate application reliability, response times, and user satisfaction.
Dependency Mapping and Analysis:

Identify and document interdependencies between applications, databases, and systems, providing insights into how applications interact within the organization's IT landscape.
Assess the impact of applications on one another, especially in cases of migration or modernization projects.
Data Reporting and Visualization:

Create and maintain reports and dashboards that effectively communicate insights from application data.
Use visualization tools to help stakeholders quickly understand key performance metrics and application trends.
Stakeholder Collaboration:

Collaborate with IT teams, business analysts, and application owners to understand data requirements and deliver insights that align with business needs.
Act as a liaison between technical and non-technical stakeholders, translating data insights into actionable recommendations.
Data-Driven Recommendations:

Based on analysis findings, make data-backed recommendations for application optimizations, performance improvements, or consolidation.
Support decision-making processes regarding application retirement, migration, or reengineering based on usage and performance data.
Documentation and Knowledge Sharing:

Document data sources, analysis methodologies, and key insights, ensuring that processes are well-documented for future reference.
Share knowledge with team members and stakeholders to support ongoing data literacy and effective data use within the organization.

Required Skills and Qualifications:

Educational Background: Bachelor's degree in Computer Science, Data Analytics, Information Systems, or a related field. Relevant certifications (e.g., Google Data Analytics, Microsoft Power BI, or similar) are a plus.
Experience: Minimum of 3-5 years of experience in data analysis, with a focus on application or system data. Experience in IT or software development environments is preferred.
Technical Proficiency: Proficiency in data analytics and visualization tools, such as SQL, Power BI, Tableau, or Excel. Knowledge of application monitoring tools and platforms is a plus.
Analytical Skills: Strong ability to interpret complex data sets, identify patterns, and generate actionable insights.
Communication: Excellent written and verbal communication skills, with the ability to present findings clearly to both technical and non-technical audiences.
Attention to Detail: High attention to detail, ensuring accuracy in data collection, validation, and analysis.
Preferred Competencies:
Problem Solving: Ability to troubleshoot data-related issues and solve complex problems related to application performance and usage.
Project Support: Experience in supporting IT transformation or migration projects with data insights.
Adaptability: Ability to manage and prioritize multiple tasks in a dynamic work environment.

All profiles will be reviewed against the required skills and experience. Due to the high number of applications we will only be able to respond to successful applicants in the first instance. We thank you for your interest and the time taken to apply!

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.

Jobs

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.

Careers Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.