We’re looking for a Senior Growth Optimization Engineer to design and build the intelligence layer that drives rider growth — the systems that decide who gets what, when, and why.

You’ll be hands on building the backend systems that automate targeting, experimentation, and optimization across the rider funnel. Your work will enable the company to make data-driven decisions at scale — optimizing incentives and comms to continuously improve rider acquisition, activation and retention. 

This is a highly cross-functional role at the intersection of backend engineering, data science, and product experimentation. You’ll ensure that growth decisions are measurable, repeatable, and continuously improving over time.

You will:

• Design and implement backend services and data pipelines that power targeting, experimentation, and incentive optimization.

• Work with data science to productionize models into various growth surfaces (e.g., churn prediction, message timing optimization, LTV scoring).

   

• Build automation frameworks to determine which users to message, through what channel (push, email, in-app), and at what cadence — ensuring we don’t spam or over-message users.

• Integrate with experimentation and analytics platforms to make growth initiatives measurable, testable, and repeatable.

• Develop feedback loops and metrics systems that monitor conversion, spend efficiency, and campaign ROI.

You have:

• 5 years of backend or data-platform engineering experience. Strong programming skills in C++, Java, or Python.

• Solid grasp of data architecture concepts — ETL, streaming pipelines, data quality, and feature stores.

• Strong SQL and analytics proficiency — comfortable exploring datasets, defining metrics, and validating hypotheses.

• Proven experience working closely with Data Science teams to productionize models or decision systems (e.g., recommendation, targeting, budget optimization).

We prefer:

• Experience in the ride-hailing, delivery, or marketplace domain, especially around incentives, dynamic pricing, or growth automation.

• Exposure to campaign orchestration systems (Braze, OneSignal, Firebase, custom pipelines) and multi-channel communication logic.

• Familiarity with causal inference, LTV modeling, or offline experimentation analysis.

• Background integrating data science workflows into production environments with CI/CD and model lifecycle management.

#LI-Hybrid