Senior Data Engineer - DV Cleared

Fortice
London
5 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

As a Data Specialist at this tech scaleup in London, you will analyse, collect, sort, and create data solutions that integrate across multiple products. You will steer decisions regarding data and build the models that power their products.


You will take client insights and requirements and make them a reality, by developing datasets and operational models to drive innovation into the product development process. You will interpret your client’s objectives, desires, and preferences to help the wider technical team understand the opportunities and apply data engineering responsibilities consistently.


You will be working on an interesting range of projects that deliver to National Security customers and as such, you will have to hold the highest level of UK Security Vetting (DV), upon application.


Key Responsibilities:


You will blend Data Engineering and Data Science, and will have experience that might cover a number of the tasks listed below:


Data Engineering tasks

  • Manage the implementation and development of integrations between the data warehouse and other systems.
  • Create deployable data pipelines that are tested and robust using a variety of technologies and techniques depending on the available technologies (Nifi, Spark)
  • Build analytics tools that utilise the data pipeline to provide actionable insights into client requirements, operational efficiency, and other key business performance metrics.
  • Complete onsite client visits and provide excellent customer support service.
  • Problem-solve in a pragmatic way, showing direction and technical support for clients whilst being agile in the approach and methodology.


Data Scientist tasks

  • Build robust, containerised data science capabilities which are scalable across projects and products (Docker, Kubernetes)
  • Collaborate with technical teams to write production-ready code, ensuring ML and AI models are deployable for your clients and projects.
  • Collaborate with software engineers to design and deploy machine learning services that are accessible via APIs for use in GUIs or direct access.
  • Research, analyse and apply data sets using a variety of statistical and machine learning techniques.
  • Support the analytical needs of the technical team inclusive of cleansing, mapping, statistical inferences, feature engineering and the bespoke data visualisation methods required by each project.
  • Review the execution of software solutions and how these perform for the business and your clients, establishing key findings and commercially minded resolutions.
  • Work with the Business Development team to create proposals and bids for new work.


Benefits:


  • £65,000 - £85,000 base + package
  • The business offers genuine autonomy and flexibility.
  • You'd work hybrid working (to client site, as there is no office)
  • You will manage your hours, core hours are 10am - 2pm – the other hours you work are up to you.
  • 27 days holiday plus bank holidays and a generous Maternity/Paternity policy.


Process:


  • On applying, you can expect a two-stage process which is typically completed in two weeks. You will first have a virtual meeting with the Head of Engineering. This will be a refreshingly open discussion around the company and their journey so far as well as a chance to speak more openly about you and what you enjoy doing.
  • Following this, a final stage 60-minute video call or face-to-face will be arranged where you can expect to delve further into your background and technical skill-set as well as scoping out what a potential role would look like.


Equality and Inclusion:


Fortice are committed to creating diverse and inclusive teams. Fortice strongly encourages people of all identities and communities to apply to the roles we advertise, it may well be that you could be more suited to a different opportunity.


Regardless of background, race, religious beliefs or sexual orientation, fortice exists to enable good people, to do better work with greater outcomes.

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 to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.