Solutions Engineer

Stack

Responsibilities

• ▹Run technical discovery calls to understand prospects' pricing models, billing infrastructure and integration requirements
• ▹Lead and own product demos, building bespoke demo environments tailored to each prospect's use cases rather than off-the-shelf scripts
• ▹Manage technical evaluations and cloud trials end-to-end: configure demo data, set up trial environments, run technical workshops, and stay close to the prospect throughout
• ▹Write clear, honest deal debriefs capturing what landed, what's at risk, and what actions are needed, shared with the AE and founder team
• ▹Surface and work through product gaps creatively: propose API-level workarounds, validate feasibility with engineering, and frame limitations transparently
• ▹Produce competitive technical analysis and maintain battle cards as Lago's market position evolves
• ▹Partner with Account Executives on commercial strategy: know when to push for a close, when to escalate to a founder, and how to sequence the conversation
• ▹Lead onboarding for new customers' engineering teams (API integration, event schema, webhooks) and finance and ops teams (plan configuration, invoice flows, reporting)
• ▹Serve as the primary technical point of contact through the implementation phase, owning the customer relationship until go-live

Soft skills

About the company

Lago's mission is to make monetization simpler for tech companies. As the leading open-source billing platform, it helps businesses build flexible pricing models and scale with confidence. The company has raised over $22 million in funding from investors including Y Combinator and FirstMark, and its platform has around 10,000 stars on GitHub with an active, growing developer community. Customers include PayPal, CoreWeave, Synthesia and Mistral.ai, acquired with very little spend on marketing or ads. The team is described as ambitious and focused, valuing reliability and proactivity, trusting each other to own tasks and drive them to completion, encouraging fast trial and error while expecting fast learning from mistakes.