Lead Data Engineer

TodayTix Group (TTG)

Lead Data Engineer at TodayTix Group to own and scale the data platform connecting product, finance, and growth. Overseeing design, architecture, and team development in a fast-paced environment.

Posted 7/24/2026full-timeLondon • 🇬🇧 United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in leading data engineering teams and managing complex data platforms, with a strong focus on SQL, dbt, and cloud data warehousing. Capable of ensuring data quality and architectural integrity while integrating AI solutions into data workflows.

Highest-signal resume keywords

Data Engineering LeadershipDeep SQL ExpertiseDbt Project ManagementSnowflake ExperienceStakeholder Management

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

SQLDbtData ModelingData Quality AssuranceCloud Data WarehousingData IngestionData TransformationData ConsumptionArchitecture DesignCode Review

Soft Skills

Team LeadershipFeedback and Career DevelopmentStakeholder CommunicationPrioritizationProblem Solving

Tools & Technologies

SnowflakeRedshiftBigQueryBI ToolsAI Agents

Industry Keywords

Data PlatformGrowth AnalyticsAI ToolingData QualityTechnical Direction

Tech Stack

Tools & technologies

Amazon RedshiftBigQueryCloudETLSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.
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.
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).
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.

Benefits

Comp & perks

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
Birthday off
Three months of fully paid Parental Leave
Employee Charity Donation Matching
Annual Professional Development Budget
Cycle to work scheme
Employee Referral Bonus