The Senior Growth Engineer sits at the intersection of marketing technology, data engineering, and product analytics, acting as a technical force multiplier for our Growth organization. Operating within the Growth Enablement team—the connective tissue of our squads—this high-ownership role is responsible for building the foundational systems that allow our Acquisition, Activation, Retention, Sales, and Partnerships squads to move faster, measure better, and experiment with confidence.

You are equally comfortable discussing causal inference models and writing production-grade data pipelines. You will drive architecture decisions, introduce AI-augmented workflows, manage complex tracking taxonomies, and set the technical quality bar for measuring what is notoriously hard to measure in an ambiguous, privacy-constrained world.

• Marketing Technology & Integrations: Build and maintain robust server-to-server (S2S) integrations with major ad platforms (Meta CAPI, Google S2S, AppsFlyer) and own end-to-end web/app tracking parity, smart script inserts, and SKAN setup.
• Tracking Standards & Governance: Own the overarching Marketing Tracking Standards (event naming conventions, schema versioning, consent management tooling), acting as the final technical authority on tracking implementation quality.
• Attribution Product Engineering: Engineer and refactor the data infrastructure feeding our attribution models, connecting ad-platform spend APIs, matching session data, and piping profit/gross margin data back to ad networks to enable value-based bidding.
• AI-Powered Growth Engineering: Productionise ML models from notebook prototypes to monitored pipelines (CLTV scoring, causal impact tooling, non-trackable conversion models) and build AI-assisted tools like automated anomaly detection on spend or LLM-powered taxonomy systems.
• Sales Tech & AI Tooling Enablement: Build pipeline infrastructure for lead scoring, intent signal enrichment, and CRM data quality, while owning the integration layer for Sales-side AI experimentation tools (e.g., ElevenLabs, Supersonic, TDO, Moon Scale).
• Signal Enhancement: Implement advanced matching, first-party data enrichment, and probabilistic matching to maximize signal quality as third-party tracking continues to degrade.
• Cross-Squad Collaboration: Act as the technical point of contact for individual Growth squads, unblocking integration roadblocks, reviewing their instrumentation, and proactively replacing bespoke solutions with scalable, shared platform infrastructure.
• Codifying Knowledge: Document architectural decisions, integration patterns, and data standards to ensure institutional growth knowledge is structured, accessible, and built to scale over time.

• Production-Grade Software Engineering: You write clean, tested, and maintainable code, applying software design principles and Domain-Driven Design (DDD) thinking to growth and data systems.
• Distributed Systems Fluency: You are highly confident shipping integrations across REST APIs, webhooks, and event streaming, and you know how to architect around the failure modes of distributed networks.
• Modern Tracking Landscape Expertise: You possess a deep understanding of browser and in-app consent frameworks, cookie-less measurement, server-side tagging, and the technical implications of privacy changes (iOS, etc.) on attribution.
• Data & Pipeline Fluency: You speak the same language as Data Engineers and Analysts. You can write complex SQL, architect robust data models, build pipelines, and maintain an exceptional standard for data reliability.
• Commercial Stakeholder Management: You comfortably interface with revenue-facing teams (Sales, Partnerships, Growth Director) and seamlessly translate commercial hypotheses into precise technical requirements.
• Applied AI & ML Competency: You know how to wrap an ML model to make it trustworthy in production, with a sharp focus on monitoring, drift detection, fallback logic, and clear ownership.
• AI-Assisted Development: You actively leverage the frontier of modern AI engineering tooling (LLMs, embeddings, vector search, AI-assisted coding frameworks) with the engineering judgment to apply them where they add true proof of value.

• Familiarity with causal inference methods (difference-in-differences, synthetic control, uplift modelling).
• Prior experience building or scaling shared platform infrastructure in high-growth consumer or B2B SaaS businesses.
• Deep technical implementation experience with modern tag management and data activation systems (e.g., Rudderstack, HubSpot, or equivalents).