Senior / Lead Data Engineer (AI-Focused)

PaymentGenes; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A high-growth technology organization is seeking a Senior / Lead Data Engineer (AI-Focused) to architect modern data platforms that enable AI and analytics. The role involves defining scalable architecture, ensuring platform reliability, and mentoring engineers. You should have 6–10+ years of experience in data engineering, advanced SQL, Python, and expertise in cloud data warehouses. Join a forward-thinking team driving AI initiatives at scale.

Qualifications

• 6–10+ years in data engineering or related disciplines.
• Proven experience architecting and scaling modern data platforms.
• Experience enabling ML/AI production workflows.

Responsibilities

• Define and evolve the data architecture roadmap.
• Design scalable ELT/ETL frameworks using DBT and cloud data warehouses.
• Architect data foundations supporting the ML lifecycle.

Skills

Advanced SQL

Python

Workflow orchestration (Dagster preferred)

Cloud data warehouses

Data modelling for analytics and AI

API integrations

Tools

Snowflake

BigQuery

Redshift

Terraform

Docker

Job description

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

• 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
• 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
• 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
• 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
• Docker / containerisation
• Infrastructure‑as‑code (e.g., Terraform)
• Monitoring and alerting systems
• 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
• 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.