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- Tasks: Architect and scale modern data platforms for AI and advanced analytics.
- Company: High-growth international tech organisation focused on AI-enabled products.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Lead impactful projects that shape the future of AI and data strategy.
- Qualifications: 6-10+ years in data engineering with strong technical leadership skills.
- Other info: Join a forward-thinking team where AI and data drive business success.
The predicted salary is between 60000 - 84000 £ per year.
PaymentGenes is proud to be partnering with a high-growth, international technology organisation to appoint a Senior / Lead Data Engineer (AI-Focused). This is a strategic hire for a business investing heavily in AI-enabled products and advanced analytics. If you are passionate about architecting modern data platforms that power real AI at scale — this opportunity is for you. This is more than a data engineering role! You will define and scale the organisation’s data platform to power advanced analytics, machine learning, and AI-driven products. Combining deep technical expertise with architectural leadership, you’ll shape long-term data strategy while remaining hands-on in building robust, production-grade systems. You will be accountable for platform reliability, scalability, governance, and AI enablement across the business.
What You’ll Do
- Data Platform Architecture & Strategy
- Define and evolve the data architecture roadmap
- Design scalable ELT/ETL frameworks using DBT and cloud data warehouses
- Establish orchestration standards (Dagster or equivalent)
- Drive decisions across batch, streaming, and real-time pipelines
- Champion data modelling standards, semantic layers, and metric governance
- AI & ML Enablement at Scale
- Architect data foundations supporting the ML lifecycle
- Design feature stores, embedding pipelines, and AI-ready datasets
- Enable MLOps workflows (data versioning, monitoring, retraining triggers)
- Support production inference (batch and real-time)
- Evaluate and integrate emerging AI tooling where strategically valuable
- Technical Leadership
- Set best practices for testing, documentation, lineage, and observability
- Lead code reviews and mentor data & analytics engineers
- Drive CI/CD and infrastructure-as-code adoption
- Own platform reliability, performance optimisation, and cost efficiency
- Establish SLAs for data freshness and quality
- Cross-Functional Impact
- Partner with Data Science, Product, and Engineering leadership
- Translate business strategy into scalable data solutions
- Influence KPI and metric governance across teams
- Act as technical escalation point for complex data challenges
Technical Environment
- Core Expertise
- Advanced SQL & Python
- Deep DBT experience (modelling, testing, macros, documentation)
- Workflow orchestration (Dagster preferred)
- Cloud data warehouses (Snowflake, BigQuery, Redshift, etc.)
- Data modelling for analytics and AI use cases
- API integrations and ingestion design patterns
- AI / ML Infrastructure
- Feature engineering architecture
- ML pipeline and deployment workflows
- Experience supporting production ML systems
- Familiarity with embeddings, vector databases, LLM orchestration (desirable)
- Data observability and model monitoring
- Platform & DevOps
- CI/CD for data workflows
- Git-based engineering standards
- Docker / containerisation
- Infrastructure-as-code (e.g., Terraform)
- Monitoring and alerting systems
What We’re Looking For
- 6–10+ years in data engineering or related disciplines
- Proven experience architecting and scaling modern data platforms
- Experience enabling ML/AI production workflows
- Demonstrated technical leadership and mentoring
- Ability to influence senior stakeholders
You’ll Thrive If You Have:
- Architectural thinking with long-term vision
- Strong decision-making under ambiguity
- The ability to balance innovation with reliability
- Clear communication across technical and non-technical audiences
- A strong ownership mindset and accountability for outcomes
What Success Looks Like
- A scalable, reliable data platform powering AI and analytics growth
- Reduced ML time-to-production
- High levels of data quality, observability, and governance maturity
- Improved cost-performance efficiency across the data stack
- A strong, growing data engineering capability within the team
This is a high-impact leadership role within a forward-thinking technology environment where AI and data are core to the business strategy. If this excites you, please reach out to the PaymentGenes team directly via LinkedIn, who can provide more detail and insight into this company's journey into AI and the opportunity.
Senior / Lead Data Engineer (AI-Focused) in City of London employer: PaymentGenes
Contact Detail:
PaymentGenes Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior / Lead Data Engineer (AI-Focused) in City of London
✨Tip Number 1
Network like a pro! Connect with people in the industry on LinkedIn, attend meetups, and join relevant online communities. The more you engage, the better your chances of hearing about opportunities before they even hit job boards.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to data engineering and 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 brushing up on your technical knowledge and soft skills. Practice common interview questions and be ready to discuss your past experiences in detail. Remember, confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior / Lead Data Engineer (AI-Focused) in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Senior / Lead Data Engineer role. Highlight your expertise in data architecture, AI, and any relevant projects you've worked on. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and data engineering. Share specific examples of how you've architected data platforms or led teams in the past. Let us know what excites you about this opportunity!
Showcase Your Technical Skills: Since this role requires deep technical expertise, make sure to highlight your proficiency in SQL, Python, DBT, and cloud data warehouses. If you've worked with MLOps or orchestration tools like Dagster, shout about it! We love seeing those skills in action.
Apply Through Our Website: We encourage you to apply directly 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. We can't wait to hear from you!
How to prepare for a job interview at PaymentGenes
✨Know Your Data Architecture Inside Out
Make sure you’re well-versed in the data architecture roadmap and can discuss how you would evolve it. Be ready to share examples of scalable ELT/ETL frameworks you've designed, especially using DBT, as this will show your hands-on experience.
✨Showcase Your AI & ML Expertise
Prepare to talk about your experience with architecting data foundations for machine learning. Highlight specific projects where you enabled MLOps workflows or supported production inference, as this will demonstrate your capability to drive AI initiatives.
✨Demonstrate Technical Leadership
Be prepared to discuss your approach to mentoring and leading teams. Share examples of best practices you’ve set for testing, documentation, and observability, as well as how you’ve influenced senior stakeholders in previous roles.
✨Communicate Clearly Across Teams
Practice explaining complex technical concepts in simple terms. You’ll need to translate business strategies into scalable data solutions, so being able to communicate effectively with both technical and non-technical audiences is key.