Hands-on production experience across every major database platform and cloud provider, built on enterprise and federal production delivery rather than certification coursework. Not theoretical coverage. Real systems at scale.
Start a conversationAWS is the primary platform, with hands-on architecture and engineering across Azure, Oracle Cloud and Google Cloud. Commercial and government regions both, on large-scale modernization work rather than certification coursework. The services are the same either side of the partition; the compliance boundary is what changes.
RDS, Aurora, Redshift, DynamoDB, ElastiCache, EC2-native databases, CloudWatch, IAM, S3
Azure SQL, Managed Instance, Synapse, Azure OpenAI, Entra ID
Autonomous Database, Base Database, Exadata Cloud Service
Cloud SQL, BigQuery, Cloud Spanner, Pub/Sub
Hands-on experience across databases, automation, integration, observability, and the compliance frameworks that shape all of it.
Primary platform depth from legacy versions to current. Internals, replication, performance architecture, and the cross-platform migration patterns that come up repeatedly in modernization work.
Document and key-value stores chosen by access pattern, including partition and sort key design that holds up under real write volume, alongside columnar analytics platforms for warehouse workloads.
Replication design for environments that break normal assumptions, including edge systems that must hold data integrity through extended loss of connectivity. Bridging legacy sync to modern event-driven architecture.
Infrastructure as code for database provisioning, configuration management, and operational automation across compliant multi-cloud estates, with pipelines for schema change and testing.
Pipeline engineering across heterogeneous systems at multi-terabyte scale, with change data capture, bulk load, and streaming patterns, plus the alerting and recovery that keeps them running unattended.
Building the observability layer rather than just consuming it: the dashboards, alert thresholds, and telemetry pipelines that other engineers end up depending on.
Architecture and implementation of HA supporting demanding uptime commitments, with cross-platform patterns across SQL Server, PostgreSQL, Oracle, and MySQL in cloud and on-premise environments.
Security architecture aligned to whichever framework applies. Access control design, encryption, audit logging, patch management, and hardening baselines treated as design inputs rather than audit cleanup. Regulated is regulated, whether the auditor is commercial or federal.
Hands-on generative AI architecture across the full data stack: model integration, retrieval over private datasets, document intelligence, caching for latency, and automated delivery pipelines.
Tell us about the environment and we will propose the right approach.