Capability
Data Engineering
Most AI and analytics projects fail on the data, not the model. We build the pipelines, platforms, and governance that make your data reliable, timely, and ready to power everything downstream.
Proven where it counts
High-performance data engineering
Built and supported a data warehouse capable of sub-second, high-throughput queries. Designed ETL pipelines using Apache Spark Structured Streaming in the cloud.
- Apache Spark
- ETL
- Big Data
Real-time insights across unified systems
Built secure data pipelines connecting Salesforce cloud and on-prem TSA systems, giving decision-makers real-time, actionable insights.
- Salesforce
- Security
- Data Pipelines
100+ microservices, event-driven architecture
Accelerated build, deployment, and scaling timelines for a large-scale program by maintaining 150+ AWS EC2 instances and a 90-microservice polyglot containerized platform.
- Java
- GovCloud
- Microservices
What we deliver
Pipelines & Orchestration
Batch and incremental ELT built with Airflow, dbt, and Dagster — observable, idempotent, and version-controlled.
Lakehouse & Warehouse
Modeling and platform design on Snowflake, Databricks, BigQuery, and Redshift, with cost and performance tuning.
Real-Time Streaming
Event-driven architectures on Kafka, Kinesis, and Spark Structured Streaming for low-latency data.
Data Quality & Governance
Contracts, testing, lineage, and cataloging so teams can trust — and find — the data they depend on.
Migration & Modernization
Move off brittle legacy ETL and on-prem warehouses to cloud-native platforms without losing history.
Analytics Enablement
Semantic layers and curated marts that make self-service BI fast and consistent.
Approach
Engineered for trust, not just throughput.
A pipeline that moves data quickly but silently corrupts it is worse than no pipeline at all. We treat data products like software: tested, monitored, documented, and owned. That discipline is what lets your analysts and models rely on what they're given.
- Infrastructure-as-code and CI/CD for every pipeline
- Data contracts and automated quality checks
- End-to-end lineage and observability
- Cost governance baked into the design
Technologies we work in
- Snowflake
- Databricks
- Apache Spark
- Apache Kafka
- Airflow
- dbt
- AWS Glue
- BigQuery
- Redshift
- Iceberg / Delta
Common questions
Have you built high-throughput data platforms for federal agencies?
Yes. At the IRS we built and supported a data warehouse capable of sub-second, high-throughput queries, with ETL pipelines on Apache Spark Structured Streaming in the cloud. At TSA we built secure pipelines connecting Salesforce cloud with on-prem systems for real-time decision-making.
Can you work inside our security and compliance boundary?
That is our home turf. We deliver in NIST 800-53 and FISMA environments, design controls in from the start, and document as we build — so the platform and its compliance evidence arrive together.
Batch or streaming — which do we need?
Usually both, applied deliberately. Streaming earns its complexity when decisions lose value in minutes — fraud, operations, security. Batch remains the right answer for most reporting and model training. We design the mix around the decision latency your mission actually requires.