Lead Engineer

Intec Select Ltd
Greater London
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Big Data Ops Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer (GCP)

Lead Data Engineering Consultant CGEMJP00330718

Lead Data Engineer - Azure Synapse

Lead Engineer

Our long-term trusted partner, a leading financial services corporation, is hiring several Lead Engineers to provide hands-on technical leadership as they continue to move into a digital landscape. The chosen candidate must have experience working within retail/digital banking with exposure to savings/lending products and experience using Java/C#/Python, React, and Azure Cloud Services. Our client is offering a basic salary between £90,000 to £100,000 DOE + 25% bonus with additional exceptional benefits to be based in London two times per week.

Your responsibilities will include:

  • Lead the development and implementation of a modern cloud foundation and data platform that is robust, scalable, fully automated, secure, and can support the growth of the business.
  • Build Scalable Architectures: Design and implement scalable, secure, and high-performing cloud-native solutions, leveraging modern technologies.
  • API Development and Integration: Design and build secure RESTful and GraphQL APIs, ensuring seamless integration with core banking systems (e.g., Mambu) and external services like Open Banking platforms.
  • Data Engineering and Analytics: Work closely with data teams to define robust data pipelines and scalable cloud-based data platforms using tools like Apache Kafka, Snowflake, or Databricks.
  • Monitoring and Performance Tuning: Implement advanced monitoring and observability solutions using tools like Prometheus, Grafana, or Datadog to proactively identify and resolve performance bottlenecks.
  • Code and System Optimisation: Proactively analyse and optimise existing systems for improved performance, scalability, and maintainability.

Core skill set for this position:

  • Strong experience building and scaling Lending or Savings platforms, with a focus on security compliance and performance, is a must.
  • Strong experience working within the financial services industry, preferably retail banking, digital banking, or investment banking industry, is a must-have.
  • Strong experience coding in any of the following languages: Java, C#, Python, and React is a must-have.
  • Proven experience leading a team of cross functional engineers, providing coaching and mentoring whilst being hands-on is a must-have.
  • Strong technical skills and expertise in relevant technologies, such as cloud computing (Azure), microservices architecture, APIs, and data management.
  • Certifications in Cloud Computing (e.g., AWS Certified Solutions Architect, Google Professional Cloud Architect, or Azure Solutions Architect) – Essential.

Benefits:

  • 25% bonus
  • 28 days holiday
  • Holiday Purchase Scheme
  • Occasional travel
  • Health Insurance
  • 13% pension
  • Plus much more.

#J-18808-Ljbffr

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.