Senior Data/AI Engineer

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teiō

WebsiteLinkedInAll Job Openings

11 - 50 employees

🤖 Artificial Intelligence

☁️ SaaS

🤝 B2B

Artificial Intelligence • SaaS • B2B

teiō is an AI transformation partner focused on helping mid-market enterprises build production AI systems that deliver measurable business outcomes quickly. They offer a full-stack approach—starting with data foundations and moving up to applications—using AI accelerators (agentic data engineering) that automate the data engineering lifecycle to go from months to weeks. They emphasize output-based pricing (pay for outcomes), deliver discrete, fixed-price deliverables, and provide AI readiness assessments and services across strategy, data foundations, transformations, and applications. Trusted across finance, healthcare, insurance, education, agriculture, and consumer products.

📋 Description

• Embed with client teams to understand their business and data, then ship the right solution, not the fanciest one. • Build and maintain data pipelines, ETL/ELT processes, and warehouse models, plus the semantic + conversational AI layers on top. • Design data models that power client reporting and decisions. • Build real AI, not just pipelines. The data engineering is the foundation; on top of it you'll do serious AI work, from agents and RAG systems to AI-native workflows running in production. • Work AI-first. Use AI to generate and review code, automate data validation and quality checks, write documentation, and move faster, then bring those gains to your clients. • Own the client relationship for your work, with proactive updates, real scoping conversations, and risks surfaced early. • Stay current with the data and AI ecosystem and fold what works back into how we deliver.

🎯 Requirements

• 8+ years in data engineering, with a track record of delivering end to end in production. • Strong SQL and Python. • Core data engineering stack, the basics: warehouses (Snowflake, BigQuery, Databricks), modeling (dbt), orchestration (Airflow or Dagster), and a semantic layer (Cube or dbt). This is table stakes. • Agentic stack, where we go deep: LLM APIs (Claude, OpenAI), agent frameworks and protocols (LangGraph, MCP), AI coding tools (Claude Code, Cursor, Codex), and RAG with vector stores (pgvector, Pinecone). This is newer and moves fast, so we care that you can pick it up, not that you've used all of it. • You use AI tools every day in how you build, and you have real opinions about where they help and where they fall short. • Bonus: applying AI or ML to data problems like anomaly detection, data quality monitoring, or automated insights. • You can talk to a customer with the same ease with which you build datasets. • You get things done with minimal supervision. You unblock yourself, you communicate, and you treat the client's outcome as your own.

🏖️ Benefits

• 100% remote. • Outcomes, not hours. • Transparency. • Growth mindset. • Fun.