Senior Data Engineering Manager

Artefact
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Data Engineering Intern

Data Engineering intern

Data Engineering Intern

Data Engineering Intern

Senior Engineer, Data Engineering

Who we are

Artefact is a new generation of a data service provider, specialising in data consulting and data-driven digital marketing, dedicated to transforming data into business impact across the entire value chain of organisations. We are proud to say we’re enjoying skyrocketing growth.

Our broad range of data-driven solutions in data consulting and digital marketing are designed to meet our clients’ specific needs, always conceived with a business-centric approach and delivered with tangible results. Our data-driven services are built upon the deep AI expertise we’ve acquired with our 1000+ client base around the globe.

We have over 1500 employees across 22 offices who are focused on accelerating digital transformation. Thanks to a unique mix of company assets: State of the art data technologies, lean AI agile methodologies for fast delivery, and cohesive teams of the finest business consultants, data analysts, data scientists, data engineers, and digital experts, all dedicated to bringing extra value to every client.


Job Description

Artefact is a new generation of data service provider, specialising in data consulting and data-driven digital marketing, dedicated to transforming data into business impact across the entire value chain of organisations.

We are seeking a seasoned Data Engineer to lead a dynamic team, ensuring the successful implementation and maintenance of data infrastructure and analytics solutions.


Key responsibilities

  • Lead, mentor, and develop a team of junior and senior data engineers, fostering a culture of continuous learning and professional growth.
  • Oversee the end-to-end delivery of data engineering projects, ensuring they are completed on time, within scope, and to the highest quality standards.
  • Coordinate with cross-functional teams, including data scientists, analysts, and other stakeholders, to understand project requirements and deliverables.
  • Design, implement, and maintain scalable and robust data pipelines using technologies such as Databricks, MS Fabric, Python, dbt and Terraform/Terragrunt.
  • Identify areas for process optimisation within data engineering workflows and implement best practices to enhance efficiency and reliability.
  • Stay updated with the latest industry trends and technologies, recommending and integrating new tools and techniques as appropriate.
  • Implement and enforce data governance and security policies to ensure data integrity, privacy, and compliance with relevant regulations.
  • Collaborate with clients to understand their data needs and provide expert guidance on the best solutions to meet their objectives.
  • Present project updates and technical concepts to non-technical stakeholders in a clear and concise manner.


Necessary Skills

  • Proficient in Python, SQL, the Azure cloud platform (including Azure DataFactory), DBT, and Terraform with a strong ability to implement and manage data solutions using these technologies.
  • Deep understanding of data architecture, data modelling, ETL processes, and data warehousing concepts.
  • Proven experience in leading and mentoring a team of data engineers, with a track record of fostering a collaborative and high-performing work environment.
  • Strong decision-making skills and the ability to inspire and motivate team members.
  • Strong organizational skills and attention to detail.
  • Strong software engineering discipline and experience using best practice tools and processes: Git, CI/CD, Infrastructure as Code, Scrum and Agile.
  • Ability to analyze complex data requirements and translate them into effective data engineering solutions.
  • Strong problem-solving skills and the ability to think critically and creatively to overcome technical challenges.
  • Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical stakeholders.
  • Strong interpersonal skills and the ability to work effectively with cross-functional teams and clients.
  • In-depth knowledge of the latest trends and advancements in data engineering, data analytics, and AI.
  • Deep understanding of data governance, data security, and compliance requirements.


Qualifications

  • A bachelor’s degree in Computer Science
  • 5+ years of professional experience in the related field

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.