Senior Data Engineer

MOTT MACDONALD
London
2 days ago
Create job alert

Location/s: London
Recruiter contact: Nikki George

Mott MacDonald is a global engineering, management, and development consultancy with over 20,000 employees across more than 50 countries and 140+ offices.

We work across incredible global industries, delivering exciting work that is defining our future and making an important societal impact in the communities we serve. Our people power our performance – we succeed when they do. With countless opportunities to collaborate, learn, and grow, the possibilities for excellence are as varied as every individual.

Whether you want to grow as a subject matter expert or broaden your experience with roles across our international community, you're surrounded by global specialists who want to combine their expertise and champion you to be your best. As a proudly employee-owned business, we benefit our clients, our communities, and each other, investing in creating the right space for everyone to feel empowered, included, and valued. Whatever your ambition, Mott MacDonald is where people come to be brilliant.

About the business unit 

Mott MacDonald's support services are the driving force behind our organisation enabling us to run efficiently and effectively. The team works collaboratively to offer specialist advice, best practice and technology to all areas of our business specifically designed for our global reach.

About the Role

We're looking for a Principal Data Engineer to play a leading role in shaping and implementing our enterprise data architecture. This position is ideal for someone who combines deep technical expertise with strong stakeholder engagement skills and a strategic mindset. You'll help define and implement the data foundations that enable impactful AI and analytics solutions across our global engineering, consulting, and infrastructure business.

You'll work closely with cross-disciplinary teams, including data scientists, domain experts, and product managers, to design scalable, secure, and interoperable data systems. You'll also help establish our enterprise data ontology and lead a small team of data engineers to turn that vision into reality.

What You'll Do

  • Enterprise data pipeline design & optimisation: Design and implement robust data pipelines to map both structured and unstructured data from diverse sources into the enterprise vector store, ensuring high-quality embeddings for downstream retrieval and analysis
  • Ontology and data modelling: Lead the development of semantic data models and domain ontologies to enable data interoperability and traceability across the enterprise.
  • Team leadership: Provide technical direction and mentoring for a small team of data engineers, supporting their growth while maintaining delivery velocity.
  • Hands-On engineering: Build, evolve, and maintain scalable, secure data pipelines, APIs, and infrastructure in a modern cloud environment (Azure preferred).
  • Stakeholder collaboration: Partner with technical and non-technical stakeholders across business units to gather requirements, align roadmaps, and communicate architecture decisions effectively.
  • Governance & best practices: Promote robust data management, including lineage, observability, access control, and compliance with ethical data use.
  • Innovation & standards: Stay ahead of industry trends in data architecture, engineering, and metadata/semantic technologies—and bring them into practice where they add value.
  • Enterprise data architecture: Collaborate with other architects to define and implement data architecture patterns across systems and domains to support analytical, AI, and operational use cases.
    What You'll Bring

  • Excellent communication and stakeholder engagement skills—able to bridge technical detail and strategic business value.

  • Experience in data engineering and architecture, ideally in complex or regulated enterprise environments.

  • Expertise in designing and implementing scalable data architectures using cloud platforms (Azure preferred).

  • Strong experience with data modelling—from conceptual to logical and physical—and familiarity with data catalogues, knowledge graphs, or ontology tools.

  • Proven experience managing or mentoring other data engineers.

  • Solid programming skills in Python and SQL, and familiarity with tools such as Git, CI/CD workflows, Docker, and Kubernetes.

  • Experience with data pipeline orchestration tools (e.g., Dagster) and modern data stack components.
    Why Join Us

  • Help shape the future of responsible, high-impact data and AI solutions in infrastructure, engineering, and consulting.

  • Work alongside a mature team with a strong mandate to improve data engineering within our organisation.

  • Shape our enterprise data engineering practice from the ground up and be a foundational contributor to our overall enterprise data architecture and strategy. This is a great opportunity to have major impact on the business. Lead and mentor within a collaborative, fast-growing team focused on innovation with purpose.

  • Gain exposure to diverse, meaningful projects that create long-term social and environmental value.
    We are actively recruiting a diverse workforce that is reflective of the communities we serve. We recognise that differences in ability, skills and experience are a strength and encourage applications from people of all backgrounds.



    Agile working

    At Mott MacDonald, we believe it makes business sense for you and your manager to choose how you can work most effectively to meet your client, team, and personal commitments. We offer a hybrid working policy that embraces your well-being, flexibility, and trust.

    Equality, diversity, and inclusion

    We put equality, diversity, and inclusion at the heart of our business, seeking to promote fair employment procedures and practices to ensure equal opportunities for all. We encourage individual expression in our workplace and are committed to creating an inclusive environment where everyone feels they can contribute.

    Accessibility

    We want you to perform your best at every stage in the recruitment process. If you are disabled or need any support to enable you to apply or attend an interview, please contact us at and we will talk to you about how we can support you.

    We offer some fantastic benefits including:

    Health and wellbeing

  • Private medical insurance for all UK colleagues.

  • Health cash plan to support you with every day health costs and treatments.

  • Access to Peppy, providing free support from menopause experts for all UK colleagues.

  • A variety of wellbeing support is available through our comprehensive wellbeing program, including access for you and your family.

  • Ability to flex your salary to opt into a wide range of health benefits, many of which can be extended to your family too.
    Financial wellbeing

  • We match employee pension contributions between 4.5% and 7%.

  • Life assurance equal up to 4 x your basic salary, with an option to increase the level of cover to 6 x your salary.

  • Our income protection scheme provides a financial benefit, as well as absence and return to work support due to long-term illness or injury.

  • Flexible benefits, including increased life assurance cover, critical illness insurance, payroll saving and will writing.

  • As an independently owned business we share the financial success of the business with all our colleagues in various ways including annual bonus schemes.
    Lifestyle

  • A minimum of 33-35 days holiday each year, inclusive of public holidays and dependent on level, with the ability to buy or sell leave through our flexible benefits programme.

  • Holiday entitlement increased to a minimum of 35 days after 5 years' service.

  • Variety of employee saving schemes and discounts from high-street retailers.
    Enhanced family and carers leave

  • Enhanced family leave policies, including 26 weeks paid maternity and adoption leave, and two weeks paid paternity/partner leave.

  • Our shared parental leave matches maternity leave meaning we pay up to 24 weeks at full pay.

  • Up to five additional days leave are provided for those with significant caring responsibilities, two of which are paid.
    Learning and development

  • Primary annual professional institution subscription.

  • A broad range of opportunities to enhance both technical and soft skills through mentoring, formal training, and self-development options.
    Networks, communities, and social outcomes

  • Join a wide range of groups including our Advanced Employee Networks which support our LGBTQ+, gender, race and ethnicity, disability, and parents/carers communities.

  • Make a difference within our communities through our social outcomes.
    Apply now, or for more information about our application process, click here.

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.