What we build
DATA PIPELINE — reference architecture
Airflow + dbt + Spark
orchestration + transform
Bronze / Silver / Gold
medallion lakehouse
CDC + streaming ingest
real-time data products
- Data lakehouses — Apache Iceberg, Delta Lake, or Hudi on S3 (GovCloud) / ADLS Gen2, with Spark or Trino compute. Full ACID, time travel, schema evolution.
- ELT pipelines — Airflow, Dagster, or Prefect orchestration; dbt for SQL-based transformation with lineage and testing.
- Real-time streaming — Kafka / MSK, Kinesis, Azure Event Hubs; Flink or Spark Structured Streaming for processing.
- Data governance — column-level lineage, PII/PHI classification, access control via Ranger or Unity Catalog equivalents, audit logs.
- Analytics layers — warehouse modeling (Kimball, Data Vault), semantic layers, BI integration (Power BI, Tableau, Superset).
- Feature engineering — for ML at federal scale, because the ML is only as good as the features.
Federal Data Engineering Delivery Lifecycle
1
Source system profiling and data catalog
Wk 1-3
2
Ingestion pipeline design and build
Wk 3-6
3
Data quality rules and validation
Wk 5-8
4
Transformation and schema enforcement
Wk 6-10
5
Security tagging and access controls
Wk 8-10
6
Production cutover and monitoring
Wk 10-12
Past performance
Federal Health IT
Enterprise Data Platform
Built data ingestion and analytics platform processing millions of health records. Real-time dashboards, automated reporting, HIPAA-compliant architecture. Full past performance →
OpenLineage
Data lineage + provenance
FedRAMP
Pipeline tools authorized
Real-time
Streaming + batch hybrid
Stack
Storage
S3 (GovCloud), ADLS Gen2, Iceberg, Delta Lake, Hudi, Parquet, ORC.
Compute
Spark, Trino, DuckDB, Flink, Ray.
Warehouse
Postgres, Snowflake Government, Redshift, Synapse.
Orchestration
Airflow, Dagster, Prefect.
Transformation
dbt, SQLMesh.
Streaming
Kafka, Kinesis, Event Hubs, Pulsar, NATS.
Quality & lineage
Great Expectations, Soda, OpenLineage, DataHub.