Bright Junior Data Engineers x 2 | London | SaaS Data Platform

Holborn and Covent Garden
3 weeks ago
Create job alert

Bright Junior Data Engineers required x 2 | London | SaaS based Data Platform |

Avanti Recruitment is working with a rapidly growing software house who makes a SaaS based Data Platform for their customers. They are looking for 2x Junior Data Engineers to help build, run, and optimise the data pipelines for their platform products. They transform complex data into actionable intelligence for clients across Finance, Retail, Travel, Telco, and Healthcare sectors.

About the role:

  • Build and manage structured ETL pipelines across staging, operational, and analytical data layers

  • Apply consistent naming, schema controls, and field-level lineage

  • Support CI/CD using Git-based workflows and automation tools

  • Help integrate ML outputs into core data layers

  • Monitor data processes and implement alerting systems

    Technology stack:

  • Cloud Platforms: Azure (primary), AWS, GCP, Databricks

  • Data Tools: SQL (essential), Python, versioned metadata frameworks

  • DevOps: Git, CI/CD pipelines, automated deployment

    What they're looking for:

  • First-class STEM degree from a prestigious university

  • 1-2 years in a Data Engineering or Platform role

  • Strong SQL skills (SQL assessment is part of the interview process)

  • Experience with cloud computing platforms

  • Ability to optimize code for large-scale, high-volume datasets

    Company highlights:

  • Joining a team of 5 which is expanding rapidly with 4 more hires.

  • Their software is deployed with customers in the UK, Germany, France, and Ireland

    Career progression:

  • Exposure to multiple cloud technologies and platforms

  • Work with a variety of clients across different industries

  • Great opportunities for career progression

    Interview process:

  • Short SQL technical exercise to complete at home

  • Two stage technical interview process.

    APPLY TODAY FOR IMMEDIATE CONSIDERATION

    Target Salary: £45,000 (applications considered from £40,000 - £50,000) + benefits

    Location: Central London – Hybrid working

    Duration Permanent

    N.B. They do not offer visa sponsorship and won’t consider those on short term visas

Related Jobs

View all jobs

Senior MLOps Engineer

Big Data Engineer - AWS Redshift + Kafka - to £100k ID37553

Senior Data Engineer - AWS - £100k base + benefits ID37553

Bright Data Engineer Needed | London | SaaS | 1st Class STEM Degree

Data Scientist | London | AI-Powered SaaS Company

Geospatial Data Engineer

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.