YLD - Senior Data Practice Lead

• Hands-on delivery (core, from day one) • Doing hands-on data engineering work directly, pipelines, transformation layers, models, and the infrastructure around them, not just guiding others • Adapting to different client contexts: legacy warehouse migrations, greenfield lakehouses, transformation layers that need rescuing • Guiding the wider team and unblocking hard technical problems as your remit grows beyond your own delivery • Defining and evolving our internal standards for data work: testing, documentation, project structure, code review • Mentoring and growing data engineers across the company • Client and commercial (core, from day one) • Shaping proposals and pitches, translating client problems into a credible data approach and a defensible scope • Acting as the escalation point on live data engagements when technical judgement calls need weight behind them • Travelling to client sites for workshops, discovery sessions, and in-person relationship building, and representing YLD at external data events • Practice, talent, and external profile (growth, over the first year) • Owning the growth of YLD's data practice: capability, reputation, and headcount over time • Contributing to attracting and hiring strong data engineers into YLD • Representing YLD at data events and in the wider community as your standing in the market builds • Supporting our sales team so that data becomes something we can proactively sell, not just respond to • You'll have the following skills and experience: • 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) • You should apply if: • Confident and credible in front of clients and prospects, without needing a script, and comfortable holding a firm technical or commercial line when a client or prospect pushes back • Still able to hold up in a deep technical review today, architecture, data modelling, pipeline design, not just talk about decisions you made a few years ago • Self-motivated, and want to own building a practice, not just execute a workstream someone else has defined, and comfortable without a fully defined remit from day one • Able to work well with commercial and client services colleagues who aren't technical themselves, translating for them without talking down • Genuinely energised by variety: different clients, different stacks, different problems, rather than depth in one domain • Happy travelling regularly, client sites, workshops, industry events, wherever the work and the relationship-building actually happens • Looking for a role that combines hands-on delivery with client-facing exposure and external visibility (speaking, writing, community), not just one of these