Lead Data Engineer, TodayTix Group

• If you thrive in a fast-moving, high-ownership environment, measure your impact by the team’s, not just your own, and care as much about business impact as technical craft, we’d love to hear from you — especially if, like the rest of the team, you reach for AI by default
• 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 judgment. 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 modeled
• 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
• You don't need to tick every box. If most of this sounds like you, we'd love to hear from you

Desirable

• 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
• You lead by setting direction, raising the team’s bar, and owning delivery, quality, and stakeholder trust across a high-demand roadmap
• The foundation is built — your job is what gets built on top of it as the org scales
• TTG is expanding as part of MARI, bringing new portfolio companies’ event and ticketing data onto the platform; PxT (our partner-facing product) is growing its surface area; and the business keeps adding markets and brands
• You’ll design how the platform absorbs that growth — new sources, new consumers, higher stakes — without the architecture buckling
• 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
• 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
• A faster, more autonomous team → Engineers ship models confidently without you reviewing every line; your reports are visibly leveling 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