Senior Data & AI Platform Engineer (AWS, Snowflake, Vector Search)

Senior Data & AI Platform Engineer (AWS, Snowflake, Vector Search) REVENUEBASE: - We're building the data infrastructure that makes AI agents trustworthy instead of error-prone. - We provide continuously refreshed, verified B2B data for autonomous AI agents and GTM workflows. - We've tripled growth while maintaining 100% gross dollar retention and staying cashflow positive. - We power AI agents for Clay, Zoominfo, Dun & Bradstreet, and the next generation of AI GTM tools. ABOUT THE ROLE We are looking for a Senior Data & AI Platform Engineer to build internal tools and services on top of our large-scale data infrastructure. Your primary focus will be developing systems that leverage vector embeddings, LLM APIs, and semantic search to unlock value from structured and unstructured data. This is a hands-on engineering role for someone who enjoys building practical AI-powered tools — not just experiments — and shipping them into production in a fast-moving startup environment. WHAT YOU’LL DO - Design and build data-driven tools that operate on large datasets stored in S3 and Snowflake - Implement pipelines that: - Extract specific columns or datasets from Snowflake - Generate vector embeddings via APIs such as OpenAI - Store and manage embeddings in vector databases like Pinecone - Enable semantic search and similarity-based retrieval - Develop enrichment workflows that: - Query structured data - Use LLM APIs to generate new derived columns - Write enriched results back into Snowflake - Build reusable internal services and SDKs around embedding generation, prompt orchestration, and data augmentation - Optimize performance and cost across AWS infrastructure - Work closely with product and data teams to turn use cases into scalable engineering solutions - Ensure reliability, observability, and maintainability of AI-powered pipelines EXAMPLE PROJECTS - Tool to extract a single Snowflake column, generate embeddings, push to Pinecone, and expose a semantic search API - Batch enrichment pipeline that queries records from Snowflake, calls OpenAI APIs for structured enrichment, and writes new columns back - Internal framework for LLM-based data transformation and validation - Query abstraction layer to make AI-enhanced analytics accessible to non-engineering teams REQUIRED QUALIFICATIONS - 5+ years of software engineering experience - Strong backend engineering skills (Python preferred; other modern languages acceptable) - Solid experience with: - AWS (IAM, Lambda, ECS/EKS, S3, networking, security best practices) - Data warehousing (Snowflake preferred) - API design and distributed systems - Hands-on experience working with LLM APIs (e.g., OpenAI) and embedding workflows - Experience with vector databases (Pinecone or similar) - Strong understanding of data modeling, ETL/ELT patterns, and performance optimization - Production experience in at least one startup environment - Ability to operate independently and ship high-impact systems end-to-end NICE TO HAVE - Experience building internal developer platforms or data tooling - Familiarity with prompt engineering and evaluation pipelines - Experience with orchestration frameworks (Airflow, Prefect, Dagster) - Exposure to retrieval-augmented generation (RAG) systems - Infrastructure-as-code experience (Terraform, CDK) - Experience managing large-scale embedding refresh and re-indexing workflows WHAT SUCCESS LOOKS LIKE - Engineers and analysts can easily leverage AI-powered data enrichment - Embedding-based search works reliably at scale - New AI use cases can be implemented quickly using shared internal tooling - Systems are robust, observable, and cost-efficient WHY JOIN US? - Work on practical, production-grade AI systems - Direct impact on how data is leveraged across the company - Startup speed with real ownership and autonomy - Opportunity to define the internal AI platform from the ground up