Data Engineer

We are hiring a Data Engineer to help build Relayer’s data foundation from the ground up. You will own the pipelines, warehouses, and infrastructure that power our AI workflows, analytics, and predictive intelligence. You will work directly with the CEO, engineers, and AI engineers to make sure the data layer is fast, reliable, and ready for the kinds of intelligence we are building on top of it.

What You’ll Build

• Production data pipelines that ingest, transform, and sync data across multi-tenant systems with reliability and observability built in
• Data infrastructure to power AI workflows, including embeddings pipelines, vector stores, retrieval systems, and the plumbing that makes RAG and agent context actually work at scale
• Analytics and warehouse architecture that supports both customer-facing dashboards and internal decision-making
• Integrations with complex enterprise business systems with real-time and batch data synchronization
• Data quality, monitoring, and lineage tooling so we catch issues before customers do
• Schema design and modeling for a multi-tenant SaaS platform with enterprise-grade security and SOC 2 compliance, * Direct impact on technical strategy and company direction as an early member of a founding team
• A massive, underserved market ripe for AI-native transformation
• AI-first architecture from day one so you are building the future, not retrofitting the past
• A team that has actually operated in this industry with co-founders who built products at Twilio and GitLab

Do you have experience in SaaS platforms?, * 2+ years building production data pipelines in a professional setting

• Strong SQL fundamentals and deep comfort with PostgreSQL
• Fluency in Python for data work, plus enough TypeScript/JavaScript to integrate with our application stack (Next.js, tRPC, Prisma, Bun)
• Hands-on experience with modern data tools such as dbt, Dagster or Airflow, and streaming systems like Kafka or equivalents
• Comfort working with AI/ML data infrastructure, including embeddings, vector databases (pgvector, Pinecone, or similar), and the data flows behind LLM-powered products
• Experience with B2B SaaS, ideally multi-tenant platforms with role-based access, audit trails, and data isolation
• Startup mindset, meaning you take ownership, move fast, communicate clearly, and care about outcomes over process

Nice to Have

• Familiarity with enterprise compliance requirements such as SOC 2, CCPA, or audit trail implementation
• Experience with AI-assisted development tools like Cursor and Claude Code
• Background working with call, voice, or other unstructured customer interaction data (transcripts, recordings, conversation logs)
• Data warehouse experience with Snowflake, BigQuery, or similar