Senior Data Engineering Manager

ZipRecruiter
London
1 month ago
Applications closed

Related Jobs

View all jobs

Head of Data Engineering

Software Team Manchester

Senior Data Developer

Analytics Engineering Manager

Senior Manager, Modern Data Engineering

Data Science Manager

Job Description

Who we are

Artefact is a new 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

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

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

Necessary Skills

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

Qualifications

  1. A bachelor’s degree in Computer Science
  2. 5+ years of professional experience in the related field

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

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.

Jobs

Machine Learning Jobs in the Public Sector: Opportunities Across GDS, NHS, MOD, and More

Machine learning (ML) has rapidly moved from academic research labs to the heart of industrial and governmental operations. Its ability to uncover patterns, predict outcomes, and automate complex tasks has revolutionised industries ranging from finance to retail. Now, the public sector—encompassing government departments, healthcare systems, and defence agencies—has become an increasingly fertile ground for machine learning jobs. Why? Because government bodies oversee vast datasets, manage critical services for millions of citizens, and must operate efficiently under tight resource constraints. From using ML algorithms to improve patient outcomes in the NHS, to enhancing cybersecurity within the Ministry of Defence (MOD), there’s a growing demand for skilled ML professionals in UK public sector roles. If you’re passionate about harnessing data-driven insights to solve large-scale problems and contribute to societal well-being, machine learning jobs in the public sector offer an unparalleled blend of challenge and impact. In this article, we’ll explore the key reasons behind the public sector’s investment in ML, highlight the leading organisations, outline common job roles, and provide practical guidance on securing a machine learning position that helps shape the future of government services.

Jobs

Contract vs Permanent Machine Learning Jobs: Which Pays Better in 2025?

Machine learning (ML) has swiftly become one of the most transformative forces in the UK technology landscape. From conversational AI and autonomous vehicles to fraud detection and personalised recommendations, ML algorithms are reshaping how organisations operate and how consumers experience products and services. In response, job opportunities in machine learning—including roles in data science, MLOps, natural language processing (NLP), computer vision, and more—have risen dramatically. Yet, as the demand for ML expertise booms, professionals face a pivotal choice about how they want to work. Some choose day‑rate contracting, leveraging short-term projects for potentially higher immediate pay. Others embrace fixed-term contract (FTC) roles for mid-range stability, or permanent positions for comprehensive benefits and a well-defined career path. In this article, we will explore these different employment models, highlighting the pros and cons of each, offering sample take‑home pay scenarios, and providing insights into which path might pay better in 2025. Whether you’re a new graduate with a machine learning degree or an experienced practitioner pivoting into an ML-heavy role, understanding these options is key to making informed career decisions.

🍪 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.