At a Glance
- Tasks: Lead the transformation of data pipelines using cutting-edge Agentic AI technologies.
- Company: Join a forward-thinking tech firm focused on AI and data innovation.
- Benefits: Enjoy a competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Collaborative environment with strong potential for career advancement.
- Why this job: Shape the future of data architecture and make a lasting impact in AI transformation.
- Qualifications: Expertise in Python, PySpark, and experience with AWS and Apache Airflow required.
The predicted salary is between 80000 - 100000 £ per year.
UST are currently recruiting for a permanent Data Architect with experience working with Agentic AI to work with one of our clients on an AI transformation project.
Location: London
Hybrid: 3 days per week in the office
About this role
The successful candidate will shape the technical direction of large, complex Data & Digital Transformation programmes and deliver engineering Agentic AI systems at scale, to unlock Transformation pace, quality and impact.
You will be undertaking a monumental Transformation to unlock organisational pace and Data agility, moving to a scalable Cloud Data architecture centred around AWS Glue (Apache Spark) & Apache Airflow, with a key focus on re-engineering numerous complex Data pipelines to the new framework by leveraging Agentic AI, all whilst embedding great Data Management to build deep trust in Data.
We're looking for a Staff-level Data & Agentic AI engineer to join the programme - driving the agentic AI based conversion framework & Data pipeline modernisation, partnering across a Senior team of hands-on Data Engineers, and driving the programme to completion with the quality and rigour that the Data engineering organisation can own and easily maintain into the future.
What you’ll do
- Shape the technical direction for the Data pipeline modernisation and re-engineering programme:
- Define architecture, standards, engineering approach, and testing framework
- Ensure coherence across all workstreams from discovery through to validated delivery
- Architect and build the Claude Code agentic workflow:
- Parse existing data movement pipelines and logic
- Generate equivalent PySpark transformations
- Produce Airflow DAGs
- Operate, iterate, and scale across the full data pipeline estate
- Ensure rigorous creation and management of the PySpark data processing layer within AWS Glue:
- Support data pipeline re-engineering
- Ensure performance, standardisation, and cost efficiency
- Design, write, and refine prompt architecture and context management logic:
- Govern Claude Code’s output
- Ensure consistent conformance to the target framework across complex conversions
- Build automated test harnesses:
- Validate functional equivalence between existing and new pipelines
- Ensure business logic is preserved throughout modernisation
- Develop structural validation and static analysis tooling:
- Evaluate AI-generated output for correctness
- Minimise hallucination risks
- Ensure zero technical debt in modernised pipelines
- Lead and coordinate engineering team efforts:
- Align technical direction
- Resolve architectural ambiguity
- Maintain delivery momentum in a fast-paced programme
- Build and maintain stakeholder relationships:
- Engage senior leadership
- Clearly translate progress, AI output quality, and technical risks
- Enable automated, high-quality documentation:
- Cover both platform and data pipelines
- Define SLOs and data producer/consumer agreements
- Leverage AI to improve documentation and delivery speed
- Design for maintainability and transferability:
- Ensure frameworks, workflows, and pipelines are fully ownable post-programme
- Treat handover quality as a first-class engineering deliverable
What we’re looking for
- Proven experience setting technical direction and architectural standards on complex, multi-workstream programmes
- Hands-on capability to build solutions as well as design them
- Deep expertise in Python and PySpark:
- Strong track record delivering large-scale distributed data systems
- Experience with Claude Code or similar agentic AI platforms:
- Prompt engineering
- Context management
- Workflow design for scalable automated code generation in regulated environments
- Strong hands-on experience with AWS ecosystem:
- AWS Glue, S3, and related data platforms (e.g. Snowflake)
- Strong experience with Apache Airflow and DAG-based orchestration
- Experience designing configuration-driven frameworks:
- Using YAML or JSON
- Experience building automated testing for data pipelines:
- Functional equivalence testing
- Regression testing
- Proven technical leadership:
- Ability to influence without authority
- Align senior engineers across data and AI domains
- Experience working in large, regulated enterprise environments:
- Navigate governance
- Build trust while maintaining pace
- Strong business acumen:
- Ability to communicate technical concepts, risks, and delivery status to senior stakeholders
- Experience designing for maintainability and handover:
- Build systems and documentation for long-term ownership by others
Data Architect in Slough employer: UST
At UST, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our London-based team enjoys the benefits of a hybrid work model, allowing for flexibility while engaging in transformative AI projects that drive personal and professional growth. With a strong focus on employee development and a commitment to delivering impactful solutions, we empower our Data Architects to shape the future of data engineering in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Data Architect in Slough
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the industry. Attend meetups, webinars, or even just grab a coffee with someone who’s already in the role you want. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to Data Engineering and Agentic AI. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to Data Architecture. Think about how you’d tackle real-world problems they might throw at you. The more prepared you are, the more confident you’ll feel!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it shows you’re genuinely interested in joining our team!
We think you need these skills to ace Data Architect in Slough
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Architect role. Highlight your experience with Agentic AI, AWS Glue, and Apache Airflow. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how you can contribute to our AI transformation project. Keep it engaging and relevant to the job description.
Showcase Your Technical Skills:Don’t hold back on showcasing your technical expertise! Include specific examples of projects where you've set architectural standards or built complex data systems. We love seeing hands-on experience in action.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It helps us keep track of your application and ensures you don’t miss out on any important updates from us!
How to prepare for a job interview at UST
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially AWS Glue, PySpark, and Apache Airflow. Brush up on your experience with agentic AI platforms like Claude Code, as you’ll want to demonstrate your hands-on capability and understanding of these tools during the interview.
✨Showcase Your Architectural Skills
Prepare to discuss your previous experiences in setting technical direction and architectural standards. Be ready to share specific examples of how you've shaped data pipeline modernisation and re-engineering programmes, highlighting your ability to align teams and resolve architectural ambiguities.
✨Communicate Clearly with Stakeholders
Since this role involves engaging with senior leadership, practice explaining complex technical concepts in a straightforward manner. Think about how you can translate progress and risks into language that non-technical stakeholders can understand, ensuring you can build trust and maintain momentum.
✨Prepare for Problem-Solving Scenarios
Expect to face some technical challenges or case studies during the interview. Prepare by thinking through how you would approach common issues in data pipeline re-engineering, such as minimising hallucination risks or ensuring functional equivalence in testing. This will show your analytical skills and readiness to tackle real-world problems.
