revenuebase-inc - Senior Data & AI Platform Engineer (AWS, Snowflake, Vector Search)
Requirements
• 5+ years of software engineering experience • Strong backend engineering skills (Python preferred; other modern languages acceptable) • 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 • 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
Responsibilities
• 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
Benefits
• 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
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