AI-Powered Enterprise Data Platform Development

**Title:** Build Enterprise AI Data Platform (CloudObjectIQ) using DuckDB, AI, and Multi-Source Data Connectors

We are building **CloudObjectIQ**, an enterprise AI-powered data platform that combines serverless analytics, metadata management, AI-assisted SQL, and document intelligence.

### Project Overview

We are looking for an experienced architect/full-stack engineer to build a scalable platform that allows users to connect to multiple enterprise data sources, query data using DuckDB, manage metadata, and interact with data using natural language.

### Phase 1 Requirements

#### Data Connectors

* Oracle
* SQL Server
* PostgreSQL
* MySQL
* SAP
* REST APIs
* CSV
* Excel
* Parquet
* Iceberg
* Delta Lake
* Amazon S3
* Azure Data Lake Storage (ADLS)
* MinIO

#### Metadata Catalog

* Connection management
* Database, schema, table, and column discovery
* Primary and foreign keys
* Data lineage
* Schema evolution
* Query history
* Data profiling
* Business glossary
* Tags and classifications

#### Query Engine

* DuckDB as the execution engine
* Direct querying of Parquet, CSV, Iceberg, and Delta Lake
* Cross-source joins
* Query optimization
* Query history and execution statistics

#### AI Features

* Natural language to SQL
* SQL explanation and optimization
* Documentation and PDF search (RAG)
* AI-powered data discovery
* AI assistant using GPT APIs
* Vector search using Milvus

#### Security

* Role-based access control (RBAC)
* Row-level security
* Column-level security
* OAuth2 / Azure AD / LDAP integration
* Secret management

#### Administration

* Connection manager
* Job scheduler
* Incremental and CDC ingestion
* Monitoring and audit logs
* Performance dashboard
* Cost and usage analytics

### Preferred Technology Stack

* Java (Spring Boot)
* React
* DuckDB
* PostgreSQL
* Milvus
* MinIO
* Kubernetes
* Docker
* Redis
* Apache Airbyte or custom ingestion framework

### Deliverables

* Production-ready source code
* Well-documented architecture
* API documentation
* Docker deployment
* Kubernetes deployment manifests
* Unit and integration tests

### Required Experience

* DuckDB
* Data engineering
* Enterprise metadata management
* AI/RAG systems
* Vector databases (Milvus or similar)
* PostgreSQL
* Spring Boot
* React
* Cloud object storage (S3, ADLS, MinIO)

Please share:

1. Similar enterprise data platforms or analytics products you have built.
2. Relevant architecture examples.
3. Your proposed implementation plan.
4. Estimated timeline and milestones.