Data Lead

Leatherhead
2 months ago
Applications closed

Related Jobs

View all jobs

Data Lead Administrator

ERP Lead Data Analyst

Data Lead - Project Zenith

SAP Data Lead

Data & Analytics Lead Analyst

Big Data Lead

Data Lead

24-Month Contract

£62.50 P/ Hour (Outside IR35)

Need BPSS Clearance, Eligible for it; Living in the UK for 5 Years.

Client Hub Office 20%, Remote 20%, Leatherhead 60%

Job Description

We are constructing a multi-disciplinary technology team to deliver a critical path within a public sector Programme. This is an opportunity to join a large-scale EI programme delivering significant benefit to a large public sector department undertaking one of the largest current procurements in the public sector.

A critical role within this team is the Data Lead, who will report to the Senior Transition Manager and support develop, test and transition for the six mobilisations.

Role is for meticulous, thorough and organised data analyst who can work with BAs to ensure data dictionary is created (and mapped to current), development and test data requirements are met, data sources are identified and managed through version control, data migration and/or transition approaches and plans are thorough and delivered and all data products created or revised for handover to BAU Data Lead

Responsibilities
• Data Lead will identify, create and maintain Data Dictionary required to deliver new requirements, ensuring this is mapped to ‘as-is’ Data Dictionary to enable data migration and transition planning to be executed.
• Data Lead will review contractual specifications, detailed business requirements and functional requirements to provide a change/update/remove gap analysis of current vs new
• Data Lead will coordinate new data sources with client counterpart(s) ensuring, these are stored and utilised consistent with the security marking and/or commercial sensitivity (for contractual data sets such as Schedules of Rates) and available to development team.
• Data Lead will advise and assist in creating of synthetic data for development and testing if actual data sets not available or not appropriate for the environment
• Data Lead will identify, create and lead required governance and stakeholder relationships required to ensure data used in development and testing is accurate, appropriate and handled correctly.
• Data Lead will support Senior Transition Manager and Technical Project Manager in ensuring detailed transition planning has clear, realistic and achievable data migration and data transition tasks and milestones to support operational go lives, exit of suppliers and exit of hypercare (priority will be the transition of open work orders from incumbent to new suppliers, requiring planning, multi-organisational governance and reconciliation)
• Data Lead will support Senior Transition Manager in working directly with client in planning operational transition, ensuring client make appropriate decisions required to effect the transition (including but not limited to handling of open work orders, invoicing of part-complete or incomplete work orders by exiting incumbent)
• Data Lead will prepare all required data for transition to live
• Data Lead will take the lead in any required hypercare data reconciliation exercises.
• Data Lead will support Project Coordinator and Technical PM in identifying data risk, issues and assumptions for RAID log.
• Data Lead will support Technical PM in Quality Gateway management, including providing any data products required for the gateway assessments.

Experience Requirements
• Experience of working in a Programme environment involve data migration and transition, or a project where data is being collated, refined, and remodelled in preparation for a similar but distinct environment, taking existing data and cleansing and restructuring for migration from one IT platform to another.
• Experience of data used in CAFM software and/or delivering FM contracts.
• Experience of working with SQL and MS Excel Pivots/Macros
• Experience of gathering and presenting analytics, reports, and findings back to internal and external stakeholders
• Experience of producing materials outlining the transition of data from source to source.
• Experience of systemised processes/ workflows ensuring there is a clear synergy between data flows and business flows.
• Experience being responsible for the integrity of the data being put forward to developers for use in varying systems.
• Experience leveraging data for business insights and decision-making while ensuring data integrity and security is upheld.
• Experience of working in fast-paced projects with fixed go-live dates

Technical Skills/Knowledge
• Intermediate in MS Excel, including working understanding of macros and Pivot tables
• Basic understanding of SQL a benefit
• Some experience using Jira KANBAN or other digital KANBAN
• Good working knowledge of software, integration and hosting technologies (Azure and IaaS virtualized)

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.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.

Data Science Leadership for Managers: Strategies to Motivate, Mentor, and Set Realistic Goals in Data-Driven Teams

Data science has become a linchpin in modern business, transforming oceans of raw data into actionable insights that guide strategy, product development, and personalised customer experiences. With this surge in data-centric operations, the need for effective data science leadership has never been more critical. Guiding a team of data scientists, analysts, and machine learning engineers requires not only technical acumen but also the ability to foster collaboration, champion ethical practices, and align complex modelling efforts with overarching business goals. This article provides practical guidance for managers and aspiring leaders aiming to excel in data-driven environments. By exploring strategies to motivate data science professionals, develop mentoring frameworks, and set achievable milestones, you will be better prepared to steer your team towards meaningful, evidence-based outcomes.