Product Engineer, Platform

Pelago•New York, NY

15h•$190,000 - $200,000•Hybrid

About The Position

Pelago is hiring a Product Engineer for its Platform Product Engineering team. The role owns the foundation that Pelago’s product and data run on, including infrastructure, the event-driven data platform, reliability, and cost management. The engineer builds with downstream reporting, member-facing features, and clinical impact in mind. The role owns the event-driven data platform end-to-end, including secure and reliable pipelines that bring data from Pelago products, third-party platforms, and external partners into the warehouse. It also defines infrastructure as code and CI/CD, establishes repeatable patterns for integrating data sources and services, and delivers platform capabilities for analytics, reporting, member-facing features, and agentic workflows. The engineer is responsible for observability, alerting, on-call and incident response, and ongoing AWS and data cost optimization. The position partners with analytics, product, clinical, and commercial teams to clarify requirements and ownership boundaries. It also contributes to Pelago’s engineering practices, including AI-native development, testing, documentation, knowledge sharing, and review quality. Pelago describes its engineering culture as outcome-driven, AI-native, autonomous and accountable, cross-functional, and attentive to quality and safety in a regulated, member-impacting domain. This is a hybrid role with three days per week in Pelago’s New York City office. The stated US base pay range is $190,000–$200,000. The broader compensation package includes an incentive bonus program, stock options, comprehensive benefits, and incentive pay applicable to eligible roles. Pelago is unable to offer visa sponsorship for this position and provides interview accommodations upon request.

Requirements

Strong production experience as a backend, platform, or systems engineer in a startup or scale-up environment.
Hands-on experience with Terraform, infrastructure as code, and building and maintaining CI/CD pipelines.
Strong experience designing, securing, and operating cloud-native services on AWS.
Proficiency in Python and TypeScript (Node) across backend services, data pipelines, and tooling.
Deep experience with event-driven architectures and data platforms, such as Kafka or AWS-native streaming and messaging, including production integration of third-party data sources.
Strong observability and reliability skills, including detecting incorrect, late, or incomplete systems or data and building alerting and runbooks.
Experience optimizing hosting costs and making architecture trade-offs with spending in mind.
Familiarity with agentic workflows and systems that support them.
Strong relational database design and SQL skills.
Product sense and ability to understand downstream platform use from user or business problems.
Ability to work autonomously in ambiguous environments and make sound trade-offs with incomplete information.
Fluency with AI-augmented engineering workflows while maintaining human judgment and QA discipline.

Nice To Haves

Experience with dbt, data modeling, and how analytics teams model and consume data.
Experience in healthcare, digital health, or safety-sensitive regulated software, including handling sensitive health data.
Experience integrating healthcare data sources, such as health information exchanges, or marketing and engagement platforms.
Experience building platforms or foundational systems from 0 to 1 in an early-stage environment.

Responsibilities

Own Pelago’s event-driven data platform end-to-end, building and evolving reliable, secure pipelines from product, third-party, and external partner data into the warehouse.
Define and manage infrastructure as code and CI/CD so environments are reproducible, changes ship safely, and engineers can deploy confidently.
Build consistent patterns for adding data sources and services.
Deliver data and platform capabilities that support analytics, reporting, and member-facing product features.
Own observability and alerting, monitoring for missing, late, or incorrect data and failing services; support on-call and incident response.
Optimize AWS hosting and data costs while maintaining reliability.
Build platform capabilities that support agentic workflows, from detection through diagnosis to resolution.
Partner with analytics, product, clinical, and commercial teams, and define clear ownership boundaries between platform, modeling, reporting, and feature work.
Improve engineering practices in AI-native development, testing, documentation, knowledge sharing, and review quality.