Service
Data Engineering
We design and build the data infrastructure enterprises run their operations on — not prototypes that need a rewrite at scale.
What It Is
Pipelines, lakehouses, and data platforms built to hold up under production load. We design and build the data infrastructure enterprises run their operations on — not prototypes that need a rewrite at scale.
Key Capabilities
- Batch and streaming pipeline design (Airflow, dbt, Spark, Kafka)
- Data lakehouse architecture on Databricks, Snowflake, or Microsoft Fabric
- Legacy ETL migration and modernization
- Data modeling for analytics and ML workloads
- CDC and real-time ingestion pipelines
- Cost and performance optimization for existing platforms
The SEF Framework Applied to Data Engineering
Every data engineering engagement runs through Discover (assess current state), Design (target architecture), Engineer (build and test), Activate (deploy and monitor), and Scale (expand as needs grow) — see the full Sadbhagy Engineering Framework.