Data Engineering

Snowflake · dbt · Airflow · Delta Lake · Spark

Reliable AI requires reliable data. We embed with your team to modernize pipelines, unify semantics, and add governance so analytics and AI workloads can scale without constant break/fix cycles.

Core Delivery Areas

We focus on creating a durable data substrate that supports BI, machine learning, and automation without repeated rework.

Lakehouse design and migration planning

Snowflake, Databricks, and Delta Lake implementation

dbt model architecture and semantic layer design

Airflow / Prefect orchestration for batch + event pipelines

Streaming with Kafka / Kinesis for operational use cases

Data quality, lineage, and observability controls

Delivery Model

Assess

Audit current warehouse, ETL jobs, schema drift, and reliability bottlenecks.

Architect

Define data contracts, canonical models, lineage strategy, and governance boundaries.

Implement

Build pipelines, semantic layers, and automated tests with production SLAs.

Enable

Deliver runbooks, documentation, and internal team training for full ownership.

Ready to Stabilize Your Data Foundation?

We can embed with your data team and start delivering production pipeline improvements quickly.