National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Analytics Platform Architect (80-100%)

Swiss Re
London
11 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Engineering & Analytics

Data Analytics Service Delivery Manager

Lead Data engineer, London

Data Science Manager

Lead Data Engineering

Data Engineer

Are you an energetic customer-obsessed professional? Passionate about data and Artificial Intelligence, who loves working with teams to design and deliver exceptional products? In this role you will focus on architecting Swiss Re's Stargate platform, the largest implementation of Palantir Foundry and AIP in the insurance industry. You will empower delivery of complex data assets and applications driving key business processes across the entire company. 

Your main responsibilities: 

Make architecture decisions encompassing integration of Stargate within Swiss Re's IT landscape as well as implementation of individual applications on the platform. 

Analyse architecture of existing solutions developed on or integrated with Stargate. 

Ensure compliance of Stargate's architecture with agreed guidelines, standards, Swiss Re architectural principles, security, and governance requirements. 

Create, update and lead implementation of new standards and guidelines covering usage and implementation of Stargate. 

Stay up to date with the latest trends in distributed computing, machine learning and Palantir Foundry product to ensure that Stargate's architecture evolves with them. 

Coordinate technical implementation of individual solutions, occasionally perform code reviews of existing applications and lead end-to-end development. 

Co-design/architect interoperability between our core platforms and third-party providers by consistently applying internal standards or formulating their externally facing equivalent. 

Guide junior members of the team through a process of formulating and proposing solution architectures. 

We expect an in-depth understanding of cloud data platforms, as well as some full-stack development experience, allowing to assess interoperability and integration scenarios. Additionally, candidate should have strong leadership skills to lead activities, projects and changes across multiple user groups and stakeholders. We expect a client-oriented and service-driven attitude. To round it up, very strong communication skills in English and at multiple levels of the organization are required. 

About the team 

You will join the Data Platforms and Engineering team which is a part of the global Group Data Services (GDS) organization of the Chief Data and Analytics Officer. The team consists of enthusiastic data engineers, architects and platform specialists dedicated to realizing the full potential of Swiss Re's data. We engage with all parts of the company, enabling them to formulate and implement data ambitions, to develop group-wide data assets and to provide data-driven products and services to our clients. Stargate is a fully democratized platform that enables the Swiss Re group to perform data driven analysis and create data driven solutions across all spectrums of complexity and required technical expertise, based on Palantir Foundry. 

About you 

Essentials: 

Minimum 5+ years of working experience. 

Expert knowledge of data engineering concepts. 

Experience with Spark and Python. 

Experience with cloud-based services from major cloud providers including, but not limited to, Microsoft Azure and Amazon Web Services. 

Experience in developing interoperability concepts between platforms and components to achieve a cohesive and consistent set of analytics capabilities. 

Experience working with and understanding the needs of customers or clients (internal or external). 

Excellent verbal and written communication skills. 

Nice to have: 

Experience in insurance/reinsurance or financial industry is a strong advantage. 

Experience with Palantir Foundry. 

Track record in engineering leadership, either as a technical lead or a line manager of engineering teams. 

Bachelor or MSc degree in Computer Science, Information Systems (or similar). 

If you are up to the challenge, we are looking forward to your application! 

About Swiss Re

Swiss Re is one of the world’s leading providers of reinsurance, insurance and other forms of insurance-based risk transfer, working to make the world more resilient. We anticipate and manage a wide variety of risks, from natural catastrophes and climate change to cybercrime. Combining experience with creative thinking and cutting-edge expertise, we create new opportunities and solutions for our clients. This is possible thanks to the collaboration of more than 14,000 employees across the world.

Our success depends on our ability to build an inclusive culture encouraging fresh perspectives and innovative thinking. We embrace a workplace where everyone has equal opportunities to thrive and develop professionally regardless of their age, gender, race, ethnicity, gender identity and/or expression, sexual orientation, physical or mental ability, skillset, thought or other characteristics. In our inclusive and flexible environment everyone can bring their authentic selves to work and their passion for sustainability.

Keywords:  
Reference Code: 130393

National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.