Booking NL
Growth Engineer (For independent contractors)
We are seeking an experienced Growth Engineer to join a dedicated task force testing a new customer experience concept against the current templated baseline. You’ll own experiment velocity end-to-end: designing tests, instrumenting behavior, shipping variations, and analyzing results – blending technical execution with marketing intuition and behavioral insight.
This is a hands-on, code-shipping role embedded in a product squad. You’ll collaborate with product, engineering, data science, and design daily. Your north star: measurable conversion lift on trip transactions and revenue per trip through rapid, rigorous experimentation across messaging touchpoints.
What success looks like
Experiment velocity: Dozens of tests designed, shipped, and conclusively measured within the task force window, with clear learnings from both wins and losses.
Measurable lift: 2–3 mechanics identified that demonstrably move trip completion rate or revenue per trip, validated at statistical significance.
Instrumentation coverage: Full behavioral funnel visibility from message open through downstream booking action, including cross-vertical attach rate.
Playbook delivery: A documented, reusable set of experiment results and growth mechanics the squad can carry forward beyond the task force.
Key Responsibilities
Experimentation & Optimization
Design and run rapid A/B and multivariate experiments across messaging touchpoints (email, push)
Build and maintain an experiment backlog prioritized by expected impact, cost, and learning value
Analyze results rigorously: statistical significance, segment effects, downstream conversion — not just opens and clicks
Instrumentation & Measurement
Instrument user behavior end-to-end: message interaction through to booking action, cross-vertical attach, and revenue attribution
Define and track experiment-level KPIs aligned with squad north stars (trip completion rate, revenue per trip)
Build lightweight dashboards and alerting to surface experiment health and early signals
Technical Execution
Ship working variations yourself: messaging templates, personalization logic, behavioral triggers, copy and creative variants
Build lightweight tooling and automations to accelerate experiment cycles (scripting, data pipelines, workflow glue)
Leverage AI/LLM tools aggressively for ideation, copy generation, audience segmentation, data analysis, and workflow automation
Cross-Functional Enablement
Product & design: Translate behavioral insights into testable hypotheses and product recommendations
Data science/analytics: Partner on experiment design, power calculations, causal inference, and metric definitions
Marketing & CRM: Close feedback loops between experiment results and messaging strategy
What We’re Looking For
Growth engineering or growth marketing experience (typically 3+ years) delivering measurable acquisition or conversion improvements at scale in a product-led environment
Technical fluency: ability to ship working code (Python, JavaScript, SQL at minimum) – you’ve built scrapers, internal tools, automations, or experiment infrastructure, not just configured third-party platforms
Strong fluency in experimentation methodology: statistical significance, sample sizing, segmentation, and the difference between a metric moving and a metric mattering
Hands-on comfort with SQL and modern data stacks; ability to self-serve analytically without waiting for a data team
Understanding of user psychology and behavioral economics – you think in terms of motivation, friction, and timing, not just channels and templates
Demonstrated AI-native workflow: active use of LLMs, copilots, and generative tools as daily multipliers, with informed opinions on where they help and where they don’t
Experience with messaging/CRM systems at scale and familiarity with deliverability, personalization, and lifecycle orchestration
Exposure to multi-vertical or cross-sell environments where success means driving composite value, not single-product conversion
Background collaborating with product and data science teams to operationalize signal feedback loops between user behavior and messaging strategy
Excellent stakeholder communication across product, engineering, marketing, and analytics; bias toward shipping over planning