AI Data Engineering

The production readiness of an AI system is determined more often by the underlying data infrastructure than by the model or agent architecture above it. Retrieval-augmented systems depend on well-designed ingestion and embedding pipelines. Operational agents depend on clean, governed, access-controlled data in the systems they read and write to. Analytics workloads supporting AI feature development depend on the same lakehouse architecture that serves broader business intelligence. Most AI initiatives that underperform in production do so because the data layer was under-engineered relative to what the use case actually required.

We build this foundation for organizations deploying production AI systems. The work spans modern lakehouse architecture, ingestion and transformation pipelines, governance infrastructure, and the retrieval and vector storage systems that support agent and retrieval-augmented use cases. The team delivers on the primary enterprise data platforms including Databricks, Snowflake, and Microsoft Fabric, and integrates with the broader cloud data services ecosystem as each client's architecture requires.

Our work covers:

• Medallion architecture design and implementation across bronze, silver, and gold layers
• Pipeline development on Databricks, Snowflake, and Microsoft Fabric platforms
• Data governance including catalog design, row and column level security, and PII handling
• Performance engineering including materialized views and query optimization for AI and analytics workloads
• Vector storage and retrieval infrastructure including embedding pipelines and hybrid search
• Data quality and observability including contracts, drift detection, and lineage
• Cost engineering and unit economics management across compute and storage