Head of Engineering (Hands-on)

GNO Partners is developing the GNO Platform, a sophisticated suite of tools designed to help Amazon sellers optimize advertising, pricing, and overall performance. The platform processes large volumes of marketplace data daily from sources such as Amazon SP-API, Ads API, and Amazon Marketing Stream. This data is transformed into automated bidding optimization, placement management, pricing intelligence, and monitoring solutions that operate seamlessly around the clock. This role involves tackling complex data engineering and product challenges rather than simple CRUD applications.

Key Responsibilities
You will serve as a hands-on engineering leader responsible for day-to-day delivery, enabling the CTO to focus on architecture and product strategy. Your time will be split roughly equally between coding and managing the team. This includes sprint planning, prioritizing tasks aligned with business goals, estimating work, and removing blockers to ensure smooth progress. You will foster a strong code review culture, enforce test discipline, and maintain the reliability of ingestion pipelines and scheduled jobs in production. Managing people is a core part of the role, including conducting 1:1 meetings, onboarding new team members, facilitating growth discussions, and participating in initial hiring screens as the team expands. You will also contribute directly by shipping meaningful features, especially early on, to build trust within the team. Collaboration with the CTO will be close and cooperative, with the CTO maintaining technical vision and infrastructure strategy while you focus on execution and team leadership.

Required Qualifications
Candidates should have at least 7 years of experience as a software engineer, including a minimum of 2 years leading teams of 3 to 8 engineers while actively contributing code. Deep expertise in TypeScript and Node.js is essential, with the ability to provide detailed feedback on NestJS services and React components promptly. Experience working with data-intensive systems is critical, particularly those involving high-volume data ingestion, background job orchestration, or analytics pipelines. Proven experience managing delivery processes—planning, scoping, prioritizing, and making trade-offs—is required. The ideal candidate will be comfortable acting as the second technical voice in the organization, constructively challenging the CTO in private and aligning publicly.

Preferred Qualifications and Benefits
Experience with Temporal or similar workflow orchestration tools such as Airflow or AWS Step Functions in production environments is a plus. Familiarity with the Amazon marketplace ecosystem, including SP-API, Ads API, and seller tools, is highly desirable. Backgrounds in e-commerce, ad-tech, or analytics products will be advantageous. Additionally, experience introducing lightweight processes to teams that previously had little structure is beneficial.

What Success Looks Like in Six Months
Within six months, you will have established a predictable delivery cadence where roadmap items are shipped independently of the CTO’s daily involvement. You will have personally contributed features across the API, worker processes, and frontend. The team will have clear priorities each week, understand the reasons behind their work, and demonstrate improved quality standards.

Hiring Process
The recruitment process includes an introductory call, a technical deep dive on a real system you have built, a paired code review exercise, a team fit conversation, and finally, an offer.

Technology Stack
The platform is built end-to-end in TypeScript (strict mode). The backend uses NestJS APIs and Temporal workflows for data ingestion and background monitoring. The frontend is developed with React, TanStack Router/Query, AG Grid, and Tailwind CSS. Data storage includes Postgres (via Supabase) and AWS data warehouse technologies such as Iceberg and Athena. Integrations with Amazon SP-API, Ads API, and Marketing Stream are core to the platform. AI features powered by LangChain/LangGraph and large language models are already in production.