> Markdown version of [/jobs/ext/3772447-senior-product-engineer-full-stack-ai-data-platforms](https://www.wearedevelopers.com/jobs/ext/3772447-senior-product-engineer-full-stack-ai-data-platforms). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Product Engineer Full Stack (AI & Data Platforms) - **Company:** Cyber Sphere LLC - **Location:** New York, NY, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Artificial Intelligence, Cloud Computing, Data Transformation, Relational Databases, Document-Oriented Databases, Elasticsearch, Event-Driven Programming, Graph Database, Python (Programming Language), PostgreSQL, Microsoft OneNote, TypeScript, Web Applications, Google Cloud, LangChain, ReactJS, Large Language Models, Backend, Data Layers, Graphql, Data Management, Front End Software Development, Entity Resolution, Terraform, Data Pipelines, Airtable, Golang - **Published:** October 16, 2026 - **Apply:** https://www.dice.com/job-detail/da318a6d-cd6a-47a3-b7b5-8661534bbccc ## About the Role Hard Skills 5 8 years of full stack engineering experience building data heavy web applications. Strong proficiency in Python, Go, GraphQL, React, TypeScript, with a lean of 70% backend / 30% frontend. Experience designing and implementing data models, pipelines, and event driven services on relational databases (PostgreSQL preferred). Demonstrated ownership of features from data layer to UI, with attention to performance, scalability, and observability. Soft Skills Highly customer centric and product centric thinking; asks strong product questions. Ability to articulate the user problem, the target persona, and the value of the solution. High energy, strong communication, and positive team presence. Work Experience Clear progression at high growth, top VC backed startups. Experience building backend systems for data intensive products (e.g., Notion, Airtable, CRM like systems). Education CS or technical degree from a top 50 or reputable university. Miscellaneous Interest in the company s core product, AI research, and investor focused data platforms. Ability to work onsite in NYC (Flatiron) three days per week. Nice to Have Experience Designing GraphQL APIs over large, complex data sources. Building LLM or agentic features using LangChain, LangGraph, Groq, or similar frameworks. Search infrastructure experience with Elasticsearch. Data transformation using dbt. Cloud infrastructure on Google Cloud Platform managed with Terraform. Experience with knowledge graphs, entity resolution, or large scale web/document data extraction. Background in fintech, venture capital, CRM, or investor facing data products. Prior startup experience in a product led, high velocity engineering culture. Traits to Avoid Frequent job changes (multiple 1 year stints or contract heavy history). ## Description A venture backed startup building a leading AI driven intelligence platform for investors is hiring Senior Full Stack Product Engineers. You will own customer facing features end to end from the underlying data pipelines and models to the product experience on top of them. This role is ideal for engineers who thrive in high velocity, product led startup environments and want to ship fast, measure impact, and iterate. You ll join one of three product teams (Discovery, Flow, or Ecosystem) and work across a stack that includes TypeScript, React, Python, Go, PostgreSQL, Elasticsearch, dbt, Google Cloud Platform, Terraform, LangChain, LangGraph, Groq. Key Responsibilities Own customer facing features end to end, from backend data pipelines to frontend product experience. Build scalable, performant, observable systems across the full stack. Ship quickly, measure outcomes, and iterate based on customer impact. Partner closely with Product, Design, and GTM teams with a customer first mindset. Contribute to a high velocity engineering culture focused on quality, speed, and learning. Continually improve product usability, reliability, and performance.