Data Engineering Lead

Overview

In this role you will be the credible technical voice on data for clients and prospects, delivering hands-on data work while shaping YLD’s data practice. You will guide engagements, mentor engineers, and contribute to proposals and workshops. The position blends client-facing leadership with deep delivery, plus a chance to build the external data profile through events and writing. You will operate at the intersection of technology, business outcomes, and client relationships, in a fast-moving consultancy context.

Pay / Benefits

• Company Private Health care
• Enhanced maternity and paternity leave
• Company’s Pension Scheme
• 25 days annual holiday
• Training/Conferences allowance
• Hardware allowance

Responsibilities

• Hands-on data engineering work across pipelines, transformations, models, and infrastructure
• Adapt to diverse client contexts including legacy migrations and greenfield lakehouses
• Mentor and unblock the data engineering team as scope grows
• Define internal data standards including testing, documentation, and code reviews
• Grow and develop data engineers within the company
• Join client meetings as the data authority and shape proposals and scopes
• Escalation point on live data engagements for technical judgment
• Travel to client sites for workshops and relationship-building
• Represent YLD at external data events
• Own the growth of YLD’s data practice, reputation, and headcount
• Support sales efforts to position data capabilities proactively

Key requirements

• Strong track record in data engineering or data leadership with client-facing exposure
• Experience leading a team or function
• Comfort presenting to senior stakeholders and handling commercial pressure
• Ability to translate engineering constraints into business terms
• Willingness to build a public data presence over time
• Hands-on delivery capability in data work from day one

• Executive-level communication
• Cross-functional collaboration
• Strategic thinking and business impact orientation
• SQL performance tuning and query planning
• Python for production data systems (typed, testable)
• Data modelling decisions (dimensional, Data Vault, normalised/denormalised)

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