Hands-on production experience across the data platform and the cloud infrastructure beneath it: every major database engine, all four cloud providers, and the automation, operations and observability that keep them running. 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.
EC2, VPC, IAM, S3, RDS, Aurora, EC2-native databases, Redshift, DynamoDB, ElastiCache, CloudWatch
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, cloud infrastructure, automation, operations, 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 compute, networking, storage, IAM and database provisioning, configuration management, and operational automation across compliant multi-cloud estates, with CI/CD pipelines for infrastructure and schema change.
Design and provisioning of the environments platforms run on: VPC and subnet layout, security groups, compute sizing, storage tiers and IAM boundaries, primarily in AWS and AWS GovCloud, with Azure, Google Cloud and Oracle Cloud where the estate requires it.
Container platforms run as infrastructure rather than experiments: cluster provisioning, workload scheduling, ingress and access control, and stateful services such as Kafka operated on Kubernetes.
The operational discipline that keeps an estate healthy after go-live: scheduled patching and version upgrades, backup and restore verification, capacity planning, reliability reviews, and runbooks written for the next engineer.
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.