Lead Data Engineer

Lead Data Engineer

Location:

London

Reporting To:
Chief Technology Officer

About Us

Our client is a leading AI-powered SaaS company that helps businesses unlock the full potential of their first-party data. Their platform transforms complex data into actionable intelligence, enabling clients to drive revenue growth and optimise decision-making. They specialise in Finance, Retail, Travel, Telco, and Healthcare, working with major household-name brands. Currently serving clients in four countries with two live products and two AI solutions in development, this is an exciting time to join as they scale rapidly.

Role Overview

This is a player-manager role leading the Data Engineering function. You'll define processes, tooling, quality standards, and team structure while personally delivering on the most complex pipeline work. You'll manage and mentor data engineers, own the data infrastructure, and collaborate across Product, Data Science, Client Success, and Platform R&D. You'll also lead the adoption of AI within the engineering workflow.

Key Responsibilities

Function Leadership & Team Management

Own the Data Engineering function: standards, tooling, and delivery processes
Manage, mentor, and develop a team of data engineers including performance and development plans
Lead recruitment and be the engineering voice in cross-functional planning
Data Engineering & Architecture

Design and build high-performance ETL/ELT pipelines across operational, analytical, and AI data layers
Lead schema design, model validation, scalable partitioning, and metadata-driven SQL frameworks
Hands-on ownership of the most complex implementation work
Data Modelling & Design

Define and evolve data models powering SCV, segmentation, AI features, and analytics
Work across dimensional, event-based, and ML-aligned data structures
Dev Ops, CI/CD & AI Ops

Own CI/CD for data pipelines using Git-based workflows, deployment governance, and rollback handling
Orchestrate ML model outputs into production pipelines with resilience controls
Drive AI tooling adoption across engineering: code generation, data profiling, documentation, and testing
Implement structured logging, alerting, SLA tracking, and access control

Key Technologies

Cloud:
Azure, AWS, or GCP | SQL (T-SQL, PostgreSQL), Python, versioned metadata frameworks
Dev Ops:
Git, CI/CD (Azure Dev Ops, Git Hub Actions) | Monitoring:
Custom logging, alerts, structured failure handling
Security:
Role-based schema access, PII isolation, audit trails

Required Skills & Experience

First-class STEM degree from a prestigious university
5+ years in Data Engineering/Platform roles, 2+ years in a lead or senior capacity
Proven line management experience — not just technical leadership
Track record of building or improving engineering processes and team structures
Expertise in SQL pipelines, Python orchestration, dimensional modelling, and star/snowflake schemas

Experience with cloud data infrastructure, CI/CD automation, and metadata frameworks
Comfortable working across product, AI, and client delivery teams

Why Join?

Build and lead a Data Engineering function from the ground up
Work on real-world AI challenges for enterprise clients across multiple sectors
Direct mentorship from the CTO and Technical Architect
Competitive salary, learning budget, and fast-track growth