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Data platforms that carry mission load.

Enterprise pipelines, lakehouse architectures, real-time streaming, governed analytics — built to turn raw federal data into mission advantage.

Discuss your dataSee past performance

What we build

  • 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.

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 →

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.
Frequently Asked
Federal data engineering, answered.
Can you build a federal data lakehouse?

Yes. Open table formats (Iceberg, Delta, Hudi) on S3 GovCloud or ADLS Gen2, with Spark or Trino compute. Full ACID, time travel, schema evolution, partition pruning. Cheaper than proprietary warehouses and no vendor lock-in.

Do you handle real-time streaming for federal environments?

Yes. Kafka / MSK, Kinesis, Azure Event Hubs for transport. Flink or Spark Structured Streaming for stateful processing. All deployable on FedRAMP-authorized cloud foundations.

What about Snowflake for federal?

Snowflake Government (FedRAMP High, IL4/IL5) is a viable choice. We build on it when the buyer has standardized there. Otherwise we prefer open-format lakehouses to avoid vendor lock-in and reduce long-term cost.

How do you handle PII, PHI, or CUI in data pipelines?

Classification-first. Every pipeline tags columns at ingest (PII, PHI, CUI, FOUO, public) and enforces access controls, encryption at rest and in transit, and audit logging downstream. No dataset moves without its classification traveling with it.

Do you do data governance and lineage?

Yes. OpenLineage-compatible lineage capture across Airflow, Spark, dbt. DataHub or custom catalogs for metadata. Column-level lineage so auditors can trace every reported metric back to source.

Related capabilities
Often deployed together.
Capability

Machine Learning

Production ML that consumes the features and datasets we build.
Capability

Cloud Infrastructure

The FedRAMP-authorized cloud foundation the data platform runs on.
Capability

Agentic AI & LLM Systems

RAG and retrieval systems built on governed federal data.
1 business day response

Mission data, made mission-ready.

Enterprise-grade data platforms for federal missions.

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