Engineering Manager · Data & Cloud Architect · AI-Native Platform Engineer 📍 Denver, CO
Engineering manager with 8+ years in software engineering and 5+ years leading cross-functional teams. I design and deliver AI-native platforms, cloud data architectures, and distributed systems — with a focus on making intelligence a first-class architectural layer, not a bolt-on feature.
"I don't separate AI from engineering. Intelligence should be a layer with the same governance, testing, and delivery standards as the rest of the stack."
Designing agentic systems where the LLM is the primary execution path, not a feature. Areas of focus:
- Agent runtimes with typed tool surfaces, streaming (SSE), and per-session cost accounting
- Prompt caching and context-assembly patterns that keep first-token latency low and per-query cost predictable
- Graph-ontology-driven policy evaluation — governance rules as data, evaluated at runtime, updatable without deploys
- Deterministic recommendation algorithms paired with LLM synthesis (auditability over opacity)
- Separation of ephemeral sessions from durable conversations for horizontal scale
Decoupling ETL from platform deploys, moving from bundled migration scripts toward staged pipelines with formal migration/data-ops separation. Staged, evidence-driven adoption of orchestration (Airflow / ADF / Fabric) only when trigger criteria are met — not speculative complexity. Bronze/Silver/Gold medallion patterns with dbt for transformations; catalog + lineage once the architecture is stable enough to be worth cataloguing.
Background in MongoDB, DynamoDB, and Neo4j at scale. Multi-region HA/DR, schema design from access patterns, and migrations into modern warehouses (Snowflake, BigQuery) with event-driven ingestion patterns.
| Domain | Technologies |
|---|---|
| AI / Agents | Anthropic Claude, tool use, SSE streaming, extended thinking, prompt caching |
| Graph / NoSQL | Neo4j, MongoDB, DynamoDB |
| Cloud | Azure (AKS, Container Apps, DevOps, Key Vault, Blob, ADLS Gen2), AWS (Lambda, EMR, S3, Step Functions), GCP (Vertex AI, BigQuery) |
| Data Engineering | dbt, Hadoop, Snowflake, Spark, Airflow |
| Backend | Go, TypeScript, Python, .NET |
| Infrastructure | Kubernetes, Docker, Tilt, Terraform, Helm |
| Databases | PostgreSQL, MongoDB, DynamoDB, Snowflake, BigQuery |
| Observability | Grafana, Loki, Prometheus |
Local/prod parity is a design constraint, not an afterthought. Full Kubernetes stacks with the same manifests, namespaces, and ingress as production — so engineers break things locally before they reach the cluster.
- DP-203 — Azure Data Engineering Associate
- MongoDB Solution Architect
- MongoDB Application Delivery
- M.A. — Data Science, Regis University
- M.A. / B.S. Mathematics — University of Colorado, Denver
Tooling carries discipline, not willpower. I build engineering cultures where linters fail the build, tests block the commit, and decision records require human approval — so the team doesn't have to rely on remembering the standards. I create space for engineers to grow through certifications, new technologies, and a culture that values continuous learning.
Interested in cloud data platforms, NoSQL architecture, AI-native systems, or agentic engineering? Let's connect.