Head of Data Engineering

Cornwallis Elt Ltd
London
1 month ago
Applications closed

Related Jobs

View all jobs

Head of Data Engineering

Head of Data Engineering - Product & Plan for Better

Head of Data Engineering

Head of Data Engineering (Basé à London)

Head of Data Engineering - Product & Plan for Better (Basé à London)

Head of Data Engineering & Governance

Head of Data Engineering – Azure, Databricks, Private Markets

A global Private Equity firm are seeking an experienced Head of Data Engineering as part of a critical data transformation and modernisation programme, moving towards a data-first approach. They are currently in the process of replacing a legacy data-warehouse setup with a platform as a service model using Databricks hosted on Azure, which this role will be responsible for leading, taking line management responsibilities for their Data Engineering team.

You will take ownership for progressing their Databricks setup, from its current PoC/incubator phase through to production, ensuring a high level of optimisation and scalability to operate at a global scale.

This will also involve implementing a semantic layer within the platform for effective data management and organisation, as well as the development of associated data pipelines and real-time reporting & visualization capabilities.

Once the platform is in place, the business will then look to apply Data Science techniques with the aim of building a best-in-class data function that works in partnership with the Investment Team and actively provides deep, actionable insights to enable business growth.

The ideal candidate will demonstrate:

  • A technical background in data engineering with experience of technologies including Python, Spark, Databricks and Azure cloud.
  • Previous experience in managing the end-to-end build of an Azure Databricks platform
  • Passionate about all aspects of data (data governance, data quality management etc.) and the positive impact ‘good data’ can bring to a business
  • An understanding of Data Science techniques (ML, NLP, AI etc.) would be beneficial
  • Demonstrable experience in setting up and managing high-performing technology teams, establishing and driving best practises and being able to still engage at a technical level
  • Able to credibly and confidently engage with the business at all levels
  • A background in Financial Services (Private Markets, Investment Banking, Investment Management) is critical

If you are a data enthusiast with experience in leading high-performing teams to develop modern data platforms, then this is a genuinely exciting time to join a growing business transitioning to a data-first approach.

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.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.