Lead Data Engineer

Join TodayTix Group as a Lead Data Engineer and take ownership of scaling our data platform. In this technical leadership role, you will focus on architecture, technical strategy, and cross-functional leadership. You will design how the platform absorbs growth, ensuring it scales without strain. Your success will be measured by the platform's scalability, data quality, team autonomy, architecture stability, and stakeholder trust. Enjoy a remote-friendly work environment with a range of benefits including healthcare, flexible paid time off, and professional development opportunities.

• Concevoir et diriger l'intégration de nouvelles sources de données sur la plateforme à mesure que TTG se développe, afin que la croissance ne signifie pas une dette architecturale.
• Diriger et développer l'équipe, en assurant des entretiens individuels, des retours d'expérience et le développement de carrière.
• Protéger systématiquement la qualité des données grâce à des tests dbt, des schémas imposés par contrat et une intégration continue qui détecte les changements.
• Design and lead the onboarding of new data sources onto the platform as TTG scales, so growth doesn't mean architectural debt
• Lead and grow the team — 1:1s, feedback, and career development
• 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
• 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
• 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
• 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
• Own the data platform end-to-end — the dbt project (staging → intermediates → marts), CI/CD, and the Snowflake infrastructure it runs on
• Cloud data warehouse depth. Hands-on experience with Snowflake or a comparable MPP warehouse (Redshift, BigQuery)
• A product and business mindset. You measure success by the decisions your data enabled, not rows modelled
• 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
• 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.)
• Stakeholder management. You partner with product, finance, growth, and CX — translating messy source data into models people trust, and setting expectations as priorities shift
• 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
• 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
• Strong architecture and code review. You set technical direction through sound data-model design and give review that makes engineers better
• 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
• You don't need to tick every box. If most of this sounds like you, we'd love to hear from you
• 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/catalogue 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