Lead Data Engineer

About the Role

We’re looking for a Lead Data Engineer to join TodayTix Group and own how we scale our data platform — the backbone connecting product, growth, finance, and CX to a single source of truth, plus increasingly the substrate AI tooling across the company queries directly.

You’ll lead the team that owns the project end-to-end — staging, intermediates, and marts — plus the CI/CD pipeline and Snowflake platform underneath it.

This is a technical leadership role, not a heads-down modeling one — most of your time goes to architecture, review, technical strategy, and cross-functional leadership, with hands-on modeling a deliberate minority.

What Success Looks Like

• 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 or an AI agent.
• A faster, more autonomous team
• Engineers ship models confidently without you reviewing every line; your reports are visibly leveling up.
• Architecture that holds
• Stakeholders across product, growth, finance, and CX bring you problems, not tickets, because you communicate early and their reporting hasn’t broken under them.

What You'll Do

• 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 prioritization 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.

Nice to Have

• Experience with a customer data platform (CDP) and identity resolution across multiple event sources — directly relevant to unifying data across our portfolio companies.
• Experience with AWS DMS or another CDC/replication tool feeding a warehouse from an operational database (MySQL, Postgres).
• Experience with Looker/LookML or another BI semantic layer.
• Experience in e-commerce, ticketing, or a marketplace business with high-cardinality partner/catalog data.
• Experience building or supporting AI/LLM-facing data products — a chatbot, a RAG pipeline, an agent with database access.
• Experience with GitHub Actions-based CI/CD for data pipelines.