Data Analyst

BGIS
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Job Title:FM Data Analyst


Purpose of Job
The Data Analyst for the TCE (The Crown Estate) contract at BGIS is responsible for the management and analysis of financial and operational data within the helpdesk function. This role ensures accurate, timely, and compliant financial reporting, data management, and client support across the contract. Working closely with the Contract Performance Manager, Contract Finance Manager, Regional Teams, and sub-contractors, the FM Data Analyst plays a critical role in optimising performance, maintaining data integrity, and driving financial insights across the TCE contract’s three regions.

This role is key to delivering cohesive financial and operational data management while supporting the helpdesk function's day-to-day operations.

Key Responsibilities

Collaboration with Finance and Contract Teams:

Collaborate with the Contract Performance Manager and Contract Finance Manager to provide accurate data analysis and reporting for the TCE contract. Support budgeting and financial planning by ensuring alignment between helpdesk operations and financial strategies. Track Work in Progress (WIP) and share regular updates to ensure accurate financial and operational visibility.

Data and Financial Reporting:

Oversee financial and operational data for helpdesk activities, producing regular reports for both BGIS and the client. Ensure accurate tracking and reconciliation of financial data, supporting compliance with contractual obligations. Prepare weekly and monthly performance reports summarising key contract KPIs, financial performance, and operational trends.

Work Order and Purchase Order Management:

Manage data and financial aspects of work orders, including tracking job progression and processing costs. Oversee purchase order creation and ensure alignment with financial requirements and supplier agreements. Verify compliance of subcontractor invoices and documentation with company policies and contractual standards.

Operational Data Support:

Act as the main point of contact for helpdesk-related data and finance queries from BGIS teams and clients. Maintain and update financial and operational records in the helpdesk system to ensure data accuracy and accessibility. Support the analysis of reactive vs. planned maintenance data to drive efficiency improvements.

Compliance and Documentation:

Ensure all data and financial documentation meet Health & Safety and compliance standards. Archive and maintain site files annually, ensuring accessible and organised records. Oversee the compliance of reactive and planned maintenance records and ensure data accuracy across systems.

Process Improvement and Analysis:

Identify trends and insights from financial and operational data to recommend process improvements. Contribute to the refinement of the AWS process and other data management systems. Support the client’s goals of improving operational efficiency and financial transparency.

Client and Internal Support:

Provide timely support for financial and data queries, both internal and external. Work collaboratively with Regional Teams to ensure contract alignment and operational consistency. Support client and subcontractor relationship management through accurate data and financial insights.

Accountabilities:Reporting directly to the onsitePerformance Manager. Collaborating closely with the Contract Finance Manager to align financial and data reporting with contract goals. Ensuring accurate financial tracking, reporting, and compliance within the TCE contract’s operational framework.Person Specification

Education:

Essential: GCSEs in English and Maths. Desirable: Higher education qualifications (A-levels, HNC/D, or degree level) are advantageous.

Skills / Knowledge:

Essential: Proficiency in Microsoft Excel and other data analysis tools. Strong communication skills, particularly in financial and data-driven contexts. High accuracy in data entry, analysis, and reporting. Ability to work both independently and as part of a team. Desirable: Familiarity with Elogbooks or similar CAFM (Computer-Aided Facilities Management) systems. Knowledge of data and financial processes within the facilities management sector.

Experience:

Essential: Minimum of 3 years’ experience in data and financial analysis for large client contracts. Desirable: Experience in a helpdesk, customer service, or facilities management environment.

Aptitudes:

Detail-oriented and methodical in data analysis and financial reporting. Strong customer focus with a proactive and self-motivated approach. Ability to prioritise tasks, manage time effectively, and meet deadlines under pressure.

Additional Information

This is not an exhaustive list of all responsibilities, duties, skills, efforts, or working conditions associated with the job. The scope of responsibilities may evolve over time to meet the needs of the contract.

At BGIS we believe that diversity and inclusion is a key business driver, such that we never lose sight of its importance as it is woven into the fabric of our organisation. We are committed to maintaining a barrier-free recruitment process by providing equal employment opportunities through recruiting and retention of individuals of all backgrounds. We recognise that promoting diversity is an essential component of our continuing pursuit for organisational success!

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.