Senior Data Engineer

Anson McCade
Liverpool
4 days ago
Create job alert

Principal Data Engineer – Consulting

Location:Leeds, Bristol or London (hybrid)

Salary:£90,000 – £105,000 (depending on experience) + bonus + benefits


NOTE:Candidates for this role must be eligible forUK Security Clearance.


Are you passionate about designing modern data solutions that drive real business value? We're looking for an experienced, hands-onPrincipal Data Engineerto join our growing consulting practice. This is a fantastic opportunity to work across greenfield projects, collaborating closely with clients to deliver scalable, cloud-native data platforms and pipelines.


About the Role

You’ll lead the design and implementation of cutting-edge data architectures using AWS technologies such as Redshift, S3, Lambda, Glue, Step Functions, and Matillion. Your role will include liaising with stakeholders to shape technical solutions, driving delivery excellence, and ultimately empowering clients to take ownership of their platforms.


We're looking for someone who thrives on complex challenges, is highly self-motivated, and values a collaborative, knowledge-sharing culture. You’ll also play a key part in mentoring other engineers and contributing to best practices in data engineering and DevOps.


What You’ll Bring

  • Strong hands-on experience with AWS data services – especially Redshift, Glue, and S3
  • Strong consulting experience - strong stakeholder management and experience leading large teams
  • Heavy involvement in RFI + RFPs
  • Proficiency in data integration/ETL development, including ELT patterns and hands-on experience with Matillion
  • Skilled in handling structured and unstructured data (JSON, XML, Parquet, etc.)
  • Comfortable working in Linux and cloud-native environments
  • Strong SQL skills and experience with relational databases
  • Knowledge of CI/CD processes and infrastructure-as-code principles
  • Experience with data cleansing, metadata management, and data dictionaries
  • Familiar with modern data visualisation tools (e.g. QuickSight, Tableau, Looker, QlikSense)


Desirable Skills

  • Exposure to large-scale data processing tools (Spark, Hadoop, MapReduce)
  • Public sector experience
  • Experience building APIs to serve data
  • Familiarity with other public cloud platforms and data lakes
  • AWS certifications (e.g. Solutions Architect Associate, Big Data Specialty)
  • Interest or experience in Machine Learning


If you're ready to bring your data engineering expertise to the next level and help shape solutions that matter, we’d love to hear from you.

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Databricks

Senior Data Engineer - DV Cleared

Senior Data Engineer - MS Fabric - Remote - £70k - £75k

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.