Lead Data Engineer

Holborn and Covent Garden
3 weeks ago
Create job alert

Lead Data Engineer ( Databricks )
London - Hybrid - Remote
Permanent
£100,000 - £130,000 plus up to 20% bonus based on performance and commercial contribution

About the Role

We’re looking for a Lead Data Engineer to spearhead some of our clients most strategic Databricks engagements.

This is a senior client-facing leadership role, blending hands-on technical delivery with architectural design and pre-sales influence.

You'll be leading high-performing squads, guiding complex transformations, and working directly with senior stakeholders to bridge business needs and engineering excellence — particularly in industries like manufacturing, utilities, and aviation.

This is a key hire to support our clients expanding Databricks practice, to build capacity for future growth.

What You’ll Be Doing

  • Act as the technical lead on client engagements, owning design and delivery of data solutions in Databricks.

  • Architect robust, scalable data platforms using the medallion architecture.

  • Translate business requirements into scalable workflows, advising on data governance, quality, and security.

  • Design and implement complex data pipelines using tools like Delta Live Tables (DLT) and Unity Catalog.

  • Guide teams in implementing best practices across engineering, DevOps, and model deployment.

  • Support pre-sales activity, including shaping proposals, estimates, and technical roadmaps.

  • Provide technical leadership, mentorship, and oversight to squads of Senior and Associate Engineers.

  • Collaborate closely with Platform Engineers and Platform Architects to align infrastructure with data needs.

  • Contribute to growing the Databricks capability – from delivery frameworks to internal tooling and capability development.

  • Lead a team of data engineers, fostering a collaborative and growth-oriented environment.

  • Evaluate new data engineering technologies and strategies, assessing their relevance and fit for the organisation’s strategic goals.

  • Work closely with the commercial team to scope projects and develop proposals that align technical capabilities with client requirements.

    Essential Skills & Experience

  • 8+ years in data engineering, with at least 2+ in a technical leadership role

  • Proven experience designing and leading Databricks-based data platforms

  • Deep understanding of the medallion architecture, data lakehouse design, and transformation workflows

  • Hands-on expertise with DLT, Unity Catalog, and model deployment frameworks

  • Strong communication and consulting skills – able to lead client conversations and manage stakeholders

  • Experience in agile delivery environments and cross-functional teams

  • Commercial awareness – comfortable contributing to pre-sales, growing accounts, and engaging with commercial targets

    Desirable Skills

  • Experience in physical asset-heavy industries (e.g. utilities, manufacturing, aviation)

  • Familiarity with platform and DevOps collaboration, especially on AWS or Azure

  • Certifications in Databricks or cloud platforms (AWS/Azure)

  • Background in consulting or client delivery environments

    Why Join?

  • Join a consultancy that’s doubling down on Databricks with enterprise-grade delivery

  • Be the go-to technical leader on projects with real-world business impact

  • Shape the future of our Databricks workforce strategy and delivery model

  • Career progression into Delivery Lead, Practice Lead, or Pre-Sales Specialist

  • Competitive compensation and strong bonus structure, aligned with delivery and commercial impact

    To find out more about this high profile Lead Data Engineering position, click apply

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer - Snowflake, DBT, Airflow - London - £100k

Lead Data Engineer - Manchester - Hybrid - £75k - £80k

Lead data Engineer - Financial Markets - Day rate

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.