Data Engineer
Data Engineer
Builds and runs the data pipeline that transforms your raw sources into a clean, indexed vector store for AI applications.
The Problem
Your data lives in databases, files, and APIs — unstructured and unindexed for AI. Building the pipeline to change that requires engineering time you don't have.
Raw Data
Your data lives in databases, file stores, and APIs — none of it formatted or indexed for AI.
Every new agent project starts with the same bottleneck: getting the data ready.
Engineering Debt
Building an ETL pipeline from scratch takes weeks of engineering time you'd rather spend elsewhere.
And when data sources change, the pipeline breaks and nobody has time to fix it.
Stale Indexes
Your vector store was populated once and never updated. Your AI answers questions about last quarter.
A pipeline that runs once isn't a pipeline — it's a one-time script waiting to rot.
The Cure
A pipeline agent that ingests, transforms, and indexes your data — keeping your vector store current automatically.
ETL pipelines for structured and unstructured data
Chunking, embedding, and vector store population
Schema mapping and data quality checks
Connects to databases, APIs, and document stores
The Data Engineer agent connects to your data sources — databases, APIs, document stores — and runs the full pipeline: extract, transform, chunk, embed, and load into your vector store. It runs on a schedule, monitors for changes, and keeps your index current without manual intervention. Your AI agents get a data layer that's always fresh and always ready.
How It Works
Deploy
We map your data sources, design the schema, and build the ETL pipeline.
Integrate
Connect to your databases, APIs, and vector store — Qdrant, Pinecone, or self-hosted.
Optimize
Monitor pipeline health, refine chunking and embedding strategies, and expand to new sources.
Where This Solution Shines
Built for teams that need their data AI-ready without a dedicated data engineering hire.
RAG Pipeline
Transform raw documents, databases, and APIs into a clean, searchable vector store for your AI agents
Data Integration
Pull from Postgres, Snowflake, S3, and APIs into a unified, AI-ready data layer
Embedding & Indexing
Chunk, embed, and index your content automatically — keep the index current as data changes
Data Quality
Validate, deduplicate, and monitor data quality throughout the pipeline with full audit logging