The platform scales, it doesn't strain → New portfolio events, new surfaces, and new markets onboard onto the existing staging/intermediates/marts pattern without one-off hacks or a rebuild.

Data people trust by default → Certified marts stay accurate and documented as volume and source count grow; breaking changes get caught by CI and dbt tests, not discovered by a stakeholder in a dashboard.

A faster, more autonomous team → Engineers ship models confidently without you reviewing every line; your reports are visibly levelling up.

Architecture that holds → Your design decisions — dynamic tables vs. incremental models, how a new portfolio company's data enters the model, orchestration choices — hold up as sources and consumers multiply, and design flaws get caught in review, not production.

Trust through ownership → Stakeholders across product, growth, finance, and CX bring you problems, not tickets, because you communicate early and their reporting hasn't broken under them.

Own the data platform end-to-end — the dbt project (staging → intermediates → marts), CI/CD, and the Snowflake infrastructure it runs on.

Design and lead the onboarding of new data sources onto the platform as TTG scales, so growth doesn't mean architectural debt.

Partner with data consumers — product, growth, finance, CX, and incoming portfolio teams — to model new sources cleanly rather than bolt them on under deadline pressure.

Run intake and prioritisation across a high-demand roadmap where reporting, growth analytics, and AI-tooling initiatives compete for the same team's time — sequence against business objectives, and say no well.

Protect data quality systematically — dbt tests, contract-enforced schemas, and CI that catches breaking changes before they reach a dashboard or an AI agent.

Set technical direction for the warehouse and give the kind of code review that makes data engineers better — spotting model design flaws before they ship.

Lead and grow the team — 1:1s, feedback, and career development.

Build the factory, not just the models: keep pushing AI into how the team works, and support the org's growing use of AI agents as direct consumers of the warehouse you build.

A track record of leading engineers. 8+ years in data engineering (or software engineering with a heavy data bent), including leading a team — formally or informally — and growing the engineers around you. This is the core of the role.

Deep SQL and dbt expertise. You've built and scaled a dbt project in production — models, tests, macros, contracts — and reason about warehouse cost and performance, not just correctness.

Cloud data warehouse depth. Hands-on experience with Snowflake or a comparable MPP warehouse (Redshift, BigQuery).

Experience scaling a data platform through growth — onboarding new business units, acquisitions, or data sources onto an existing model without rearchitecting from scratch. (M&A / multi-entity data integration experience is directly relevant given the evolving company structure.)

Full-stack data platform judgement. Comfortable across ingestion (CDC/replication from operational databases, event pipelines), transformation (dbt), and consumption (BI tools, reverse ETL, AI/agent access) — even where you're not the one writing every layer.

Strong architecture and code review. You set technical direction through sound data-model design and give review that makes engineers better.

Stakeholder management. You partner with product, finance, growth, and CX — translating messy source data into models people trust, and setting expectations as priorities shift.

A product and business mindset. You measure success by the decisions your data enabled, not rows modelled.

AI fluency. You already reach for AI to write and review data models, and you're comfortable with AI agents as first-class consumers of the warehouse you build.