Data Analyst/Engineer - Global Insight

Penguin Random House
London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Analyst- Celonis Experience

Data Analyst

Supply Chain Data Analyst

Data Analyst

Data Analyst

Data Analyst

Location:London

Contract:Permanent

Type of work:Hybrid (Minimum of 2/3 days in the London office)

The Role and the team

Fremantle’s Global Insight Department is integral to analysing television performance metrics, audience viewing behaviours, and emerging content trends. Positioned centrally within the organisation, the department is unifying internal performance data and other company datasets into a consolidated Microsoft Fabric environment, enhancing data accessibility and analytical efficiency across the business.

As a Data Analyst/Engineer, you will play a pivotal role in both managing and optimising the company's data infrastructure and performing in-depth data analyses. Your responsibilities will encompass working with Microsoft Fabric, Azure Data Factory, SQL, and other tools to construct data pipelines. Additionally, you will analyse datasets to come up with insights for the business, supporting various business units in making informed decisions across the whole of Fremantle. This hybrid role is ideal for professionals who are both technically adept and analytically minded, capable of navigating the intricacies of data engineering while delivering insightful analyses.

As a company built on storytelling, we love talking about TV and film—so if you enjoy seeing how data drives the industry, and want to make a difference here, you’ll fit right in.

Key Responsibilities

  • Ingest and integrate new data sources into Fremantle’s centralised Microsoft Fabric data platform.
  • Build and maintain data pipelines to transform raw data into structured, analytics-ready datasets.
  • Perform data analyses to identify trends, patterns, and insights that inform business decisions.
  • Ensure data integrity, governance, and security within the Fabric/Azure ecosystem.
  • Collaborate closely with the Data & Analytics team to structure datasets for dashboards, reporting, and advanced analytics.
  • Work with stakeholders to understand business needs and translate them into data models and analytical solutions.
  • Automate data processes to reduce manual intervention and improve efficiency.
  • Stay current with best practices in data engineering and analysis, particularly within the Microsoft ecosystem.

Essential Skills and Experience

  • Hands-on experience with cloud-based data platforms (e.g., Azure, AWS, or Snowflake); familiarity with Microsoft Fabric or GCP is desirable.
  • Proficiency in Spark SQL and PySpark, with the ability to write complex queries and transformations for data management, optimisation, and scalable processing.
  • Experience with ETL/ELT pipelines, using tools like Azure Data Factory or similar; experience with Google BigQuery is a bonus.
  • Experience writing Python scripts for data transformation, validation, and automation.
  • Experience in retrieving and integrating data from APIs, including RESTful and JSON-based services and other data collection methods such as web scraping.
  • Understanding of data modelling principles and best practices for structuring data for analytics.
  • Ability to work with large, complex datasets across various formats, including databases, Excel files, CSV, JSON, Parquet, and APIs, while optimising performance.
  • Experience with data visualisation tools, particularly Power BI, for creating interactive dashboards and reports.
  • Nice to have – experience with Power Automate for workflow automation and Power Apps for building data-driven applications.
  • Familiarity with data governance tools like Microsoft Purview is a plus.
  • Strong problem-solving skills, with the ability to diagnose and resolve data issues.
  • Bonus if experienced with Generative AI tools, including Custom GPTs, for data processing, automation, or intelligent data augmentation.
  • Excellent communication skills, with the ability to collaborate with analysts, stakeholders, and technical teams.

Our benefits include a generous company pension, summer Fridays, audience tickets, personalised working, employee assistance programme, access to free courses and training, local discounts, free breakfast, lunch, coffee and snacks in the office, cycle to work scheme, season ticket loan, business coaching sessions and volunteer days.

If you need any of this information in a different format or would like to suggest a different form of application, please contact

Fremantle is part ofRTL Group, a global leader across broadcast, content and digital, itself a division of the international media giantBertelsmann.

For more information, please visitFremantle.com, follow us @FremantleHQor visit ourLinkedInandFacebookpages.

#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.

Jobs

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!

Jobs Careers

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.

Jobs

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.

🍪 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.