todaytixgroup - Lead Data Engineer
Requirements
• 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. • 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. • TodayTix Group takes care of our team. We’re proud to offer a generous suite of benefits. Here are some of our favourites: • Hybrid work environment (blend of in-office and at-home days) • Up to 4 weeks per year of flexible 'work from anywhere' • Generous pension match • Access to a bespoke Pension scheme • Complimentary tickets to shows and events • Employee Assistance Programme • Access to a corporate rate Vitality PMI plan • Healthcare cash plan • Season Ticket loans • Three months of fully paid Parental Leave • Employee Charity Donation Matching • Annual Professional Development Budget • Cycle to work scheme • Employee Referral Bonus
Responsibilities
• 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.
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