Senior Analytics Engineer

Harnham
united kingdom
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineering Manager

Senior Data Engineer

Analytics Engineering Manager

Senior Data Scientist

Senior Data Engineer

Senior Data Engineer

Senior Analytics Engineer

Hillingdon - Once a month in office

Up to £75,000


Company Overview

Join a fast-growing telecom startup backed by a major industry player. This company is on a mission to revolutionise the mobile industry by prioritising value, flexibility, and mutuality. With an abundance of rich data sets, this is an exciting opportunity for data professionals who thrive on working with large and complex data systems.


This mobile network is known for its unique community-led model, where members help each other with support and even contribute ideas for new features!


The Opportunity

The Data Engineering function is responsible for constructing analytics pipelines and supporting operations across different teams. Currently, they are developing a new metrics catalog within an analytical database, aiming to fully automate the process to integrate seamlessly into regular operations.


In this day to day, you will:

  • Designing and developing high-quality data pipelines using DBT and Python.
  • Migrating reports from SQL Server to Snowflake.
  • Understanding and modeling data points to implement efficient solutions in Snowflake.
  • Supporting the build-out of the data warehouse.
  • Implementing CI/CD pipelines for streamlined deployment processes.


What You Will Bring

We're looking for someone who loves working with data and enjoys solving complex problems.


Must-Haves:

  • Solid experience in data modeling (think Data Vault, Kimball, or similar approaches).
  • Extensive experience working as a Data Engineer or Analytics Engineer.
  • Strong skills in DBT and SQL – you’ll be using these every day!
  • Hands-on experience with cloud data warehouses like Snowflake, GCP, Redshift, or BigQuery.


Nice-to-Haves:

  • Familiarity with CI/CD pipelines – if you’ve set up automated deployments, even better.
  • Understanding of attribution models – knowing how to track and optimise performance is a plus.
  • Some exposure to Kafka for real-time streaming – if you’ve worked with it, that’s a great bonus!


Why Join?

This is a fantastic opportunity to be part of an innovative telecom company that values data-driven decision-making and cutting-edge technology. If you are passionate about analytics engineering and want to work with rich datasets in a collaborative environment, this could be the perfect role for you!


  • Eco-Friendly Efforts! 🌍 – The company promotes sustainability by encouraging customers to buy refurbished phones and recycle old devices through its phone recycling program.
  • Award-Winning Network! 🏆 – This mobile provider has won multiple awards for customer satisfaction and value, often beating big-name competitors in mobile network rankings.
  • Powered by a Major Network! 📡 – While operating independently, this provider runs on a well-established network, ensuring solid coverage across the UK.

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.