Lead Data Engineer / Architect – Databricks Active - SC Cleared

Farringdon, Greater London
1 month ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer / Architect – Databricks - SC Cleared
SR2 is supporting a critical greenfield transformation programme in the public sector and urgently seeks a hands-on Lead Data Engineer/Architect with deep Databricks experience to help set the direction and strategy for the project.
This is a strategic role for someone who has not only delivered but also led end-to-end Databricks implementations, ideally across multiple programmes. You’ll define the technical architecture, lead Proof of Concepts (PoCs), and build a modern data platform from scratch in a highly visible public-sector environment.
Key Responsibilities:

Act as the technical lead for a new Databricks implementation, working from greenfield through to full production deployment.
Own and deliver streaming and batch data pipelines in Databricks for complex, sensitive use cases.
Define and set architectural standards and delivery roadmaps for the data platform.
Lead PoCs with hyperscalers to assess and select appropriate data services and tooling.
Collaborate with engineering teams, stakeholders, and partners to ensure scalability, performance, and compliance.
Document best practices, decisions, and technical architecture to support future scaling and handover.Essential Experience:

2–3+ years of strong, hands-on Databricks experience, including having led implementations from setup through to production.
Demonstrable track record of delivering greenfield or ground-up data platform builds.
Strong Python skills for data transformation and orchestration.
Deep understanding of modern data architectures, pipelines, and cloud-native solutions – especially AWS.
Able to operate strategically (architecture, direction-setting) and tactically (hands-on engineering).
Active SC Clearance (must be current)

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.

Careers

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.

Jobs

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.

Education

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.

🍪 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.