Lead Data Analyst

Experis - ManpowerGroup
London
1 month ago
Applications closed

Related Jobs

View all jobs

Lead Data Analyst

Lead Reporting and Data Analyst

Senior Data Analyst

Consumer Data Manager

Data Analyst

Data Admin Specialist / Data Analyst

Job Title:Lead Data Analyst
Contract Length: Until End of Year (possibility of extension)
Location:London (Hybrid)

Job Overview:
We are seeking an experienced and driven Lead Data Analyst to join our Global Customer Service Team on a contract basis. The successful candidate will play a key role in elevating our Power BI reporting suite, driving user engagement, and identifying opportunities for a wide range of stakeholders. If you're a data-driven professional with advanced technical skills and a passion for transforming data into actionable insights, we want to hear from you!


Key Responsibilities:

  • Elevate our Power BI reporting suite by improving and enhancing the dashboards to drive user engagement and identify opportunities across various business functions.
  • Write and optimize SQL queries to extract, manipulate, and analyze data from Snowflake and Google BigQuery data warehouses.
  • Consolidate data from external partners using advanced Microsoft Excel skills (including formulas, functions, pivot tables, etc.).
  • Implement and maintain a data framework that ensures consistency, scalability, and ease of reporting.
  • Collaborate with various stakeholders across the business to understand their data needs and provide insights that help drive strategic decisions.
  • Deliver actionable insights and recommendations to senior leadership and key stakeholders in the business.

Essential Skills and Experience:

  • Advanced Power BI skills - Strong experience designing and enhancing reports and dashboards, with a focus on driving engagement and identifying insights.
  • SQL proficiency - Expertise in writing SQL queries to interact with Snowflake and Google BigQuery data warehouses.
  • Advanced Excel skills - High competency in using Microsoft Excel for complex data manipulation, consolidation, and analysis (formulas, functions, pivot tables).
  • Experience implementing and maintaining a data framework that ensures data consistency and reliability across teams.

Desirable Skills and Experience:

  • Python - While not essential, knowledge of Python would be a nice-to-have for automating tasks and data manipulation.
  • Power Automate - Experience with Power Automate or a willingness to learn and implement it to optimize processes would add significant value to the role.
  • Stakeholder management - Ability to communicate with business stakeholders to gather requirements and manage expectations (not critical, but beneficial).

#J-18808-Ljbffr

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.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.