Data Engineer London, UK

Galytix Limited
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Full stack Data Engineer

Data Engineer

Data Engineer

Senior Data Engineer AWS

Lead AWS Data Engineer

Galytix (GX) is delivering on the promise of AI.

GX has built specialised knowledge AI assistants for the banking and insurance industry. Our assistants are fed by sector-specific data and knowledge and easily adaptable through ontology layers to reflect institution-specific rules.

GX AI assistants are designed for Individual Investors, Credit and Claims professionals. Our assistants are being used right now in global financial institutions. Proven, trusted, non-hallucinating, our assistants are empowering financial professionals and delivering 10x improvements by supporting them in their day-to-day tasks.

Responsibilities:

  • Helping to architect, design, implement, and optimise our data ingestion, transformation, and spreading pipelines and processes.
  • Developing data models, processing pipelines, and back-end services supporting the data science teams, automating processes, building integrations, and analytics.

Desired skills:

  • A university degree in Mathematics, Computer Science, Engineering, Physics or similar.
  • 5+ years of relevant experience in Data Engineering, warehousing, ETL, automation, cloud technologies, or Software Engineering in data related areas.
  • Ability to write clean, scalable, maintainable code in Python with a good understanding of software engineering concepts and patterns. Proficiency in other languages like Scala, Java, C#, C++ are an advantage.
  • Proven record of building and maintaining data pipelines deployed in at least one of the big 3 cloud ML stacks (AWS, Azure, GCP).
  • Hands-on experience with open-source ETL, and data pipeline orchestration tools such as Apache Airflow and Nifi.
  • Experience with large scale/Big Data technologies, such as Hadoop, Spark, Hive, Impala, PrestoDb, Kafka.
  • Experience with workflow orchestration tools like Apache Airflow.
  • Experience with containerisation using Docker and deployment on Kubernetes.
  • Experience with NoSQL and graph databases.
  • Unix server administration and shell scripting experience.
  • Experience in building scalable data pipelines for highly unstructured data.
  • Experience in building DWH and data lakes architectures.
  • Experience in working in cross-functional teams with software engineers, data scientists, and machine learning engineers.
  • Experience in working with or leading an off-shore team.
  • Proven record of building data science environments deploying ML solutions in at least one of the big 3 cloud ML stacks (Azure/AWS/GCP) and on Kubernetes clusters.
  • Excellent written and verbal command of English.
  • Strong problem-solving, analytical, and quantitative skills.
  • A professional attitude and service orientation with the ability to work with our international teams.

Why you do not want to miss this career opportunity?

  • We are a mission-driven firm that is revolutionising the Insurance and Banking industry. We are not aiming to incrementally push the current boundaries; we redefine them.
  • Customer-centric organisation with innovation at the core of everything we do.
  • Capitalize on an unparalleled career progression opportunity.
  • Work closely with senior leaders who have individually served several CEOs in Fortune 100 companies globally.
  • Develop highly valued skills and build connections in the industry by working with top-tier Insurance and Banking clients on their mission-critical problems and deploying solutions integrated into their day-to-day workflows and processes.


#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.