Principal Data Engineer (Data Practice Lead)

YLD, London

The role

We're building out data as a first-class practice at YLD, and we need someone in London who can be the credible technical voice in front of clients and prospects, not just describing what we could do, but backing it with real delivery experience.

This is a senior role, reporting to our Head of Engineering. You'll sit alongside our commercial and client services leadership in meetings where technical credibility matters, help shape and win new engagements, and stay close enough to client work to guide it and step in yourself when an engagement needs you. Alongside, we expect you to build our external reputation in data through events, writing, and the wider community.

Leadership and commercial

Consulting or agency experience is a strong plus, but real client or stakeholder-facing exposure elsewhere is acceptable
A track record of technical leadership: leading a team or function
Comfortable presenting to senior client stakeholders and holding your own under commercial pressure
Cross-functional fluency: you translate engineering constraints into business terms and negotiate realistic commitments
Appetite to build a public presence in the data community over time, even if you don't have one yet

Technical

Not theoretical, evidenced by things you've actually shipped, and deep enough to set the bar for a team and answer a sceptical client:

SQL as engineering: read and review SQL that performs at scale, and understand query planning, engine quirks, and how materialisation choices affect cost and performance well enough to guide a team's decisions
Python for data: built and maintained production data systems in typed, testable Python, not just notebooks, and can set that standard for others
Data modelling: made deliberate choices between dimensional, Data Vault, normalised and denormalised designs, and can explain the trade-offs in flexibility, query performance, and maintainability to both engineers and clients
Transformation architecture: design transformations that are idempotent, incremental, and dependency-aware. You think in DAGs, not scripts
Pipeline design: weigh batch, streaming, and micro-batch trade-offs against latency, complexity, cost, and reprocessability, and pick the right approach for the problem
Data testing: know what to catch at build time (schema contracts, assertions, transformation logic) versus defer to observability, and can make that call for a team
Data observability: treat data reliability like site reliability, with measurable indicators, alerting, incident response, and root cause analysis
CI/CD for data: version, test, and deploy pipelines like software, with environment promotion, rollback strategies, and infrastructure as code
Data governance: implemented lineage, cataloguing, sensitive data classification, or access control, and can hold a client to account on making data auditable and secure
Cost-conscious: optimised warehouse spend, storage strategies, or job efficiency, and treat compute as a resource to manage, not ignore
Platform fluency: worked across orchestration (Airflow, Dagster), warehouses (Snowflake, BigQuery, Databricks), ingestion (Fivetran, Airbyte, custom), and transformation (dbt, Spark)
AI Native: experienced in agentic coding workflows (context, harness, loop, and graph engineering), and able to embed AI Engineering constructs into pipelines (RAG, semantic search, evals, guardrails, etc)

Principal Data Engineer (Data Practice Lead)

The role

Principal Data Engineer (Data Practice Lead)

The role

What you need

Hands-on delivery (core, from day one)

Client and commercial (core, from day one)

Practice, talent, and external profile (growth, over the first year)

Leadership and commercial

You should apply if

Pay and benefits

Our typical Recruitment Process looks like this

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About YLD

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