Selected Outcomes
Principal Engineering & Leadership Outcomes
These examples highlight the high-stakes problems I solve—whether as a strategic consultant or a permanent engineering leader: unreliable pipelines, fragile reporting, and cloud systems that need long-term governance and reliability.
Production Reliability and Data Quality
- Early-warning issue detection: Designed an Airflow, Slack, dbt, and BigQuery workflow that surfaces pipeline failures and data quality anomalies fast enough for practical triage.
- Schema drift resilience: Implemented drift detection and downstream impact workflows for external feeds, reducing breakage and shortening incident response time.
- Validation taxonomy: Defined practical checks spanning schema integrity, freshness, reconciliation, correctness, and referential consistency.
Best for teams that need stronger monitoring, validation, and a culture of operational excellence.
Analytics Modernization & Agentic AI
- Agentic dbt/BigQuery configuration: Implemented agent-augmented workflows to automate dbt model generation and BigQuery partition management, ensuring configuration consistency at scale.
- Spreadsheet to governed models: Migrated fragile Excel and Google Sheets logic into warehouse models and orchestrated jobs, improving repeatability and metric trust.
- dbt operating discipline: Enabled dbt-driven transformations in production workflows to support governed, repeatable analytics delivery.
Best for teams moving from ad hoc analytics to a governed, AI-augmented platform architecture.
Cloud Architecture and Secure Data Exchange
- Agentic cloud operations: Developed LLM-driven diagnostics for cloud-native ingestion services to identify and remediate configuration drift in GCP and AWS environments.
- Hybrid SFTP to cloud analytics gateway: Connected AWS-based SFTP silos with GCP-native analytics to create secure, automated, and auditable feed handoff.
- SURL platform: Designed and built a secure, auditable AWS transfer platform for large regulated datasets using Kafka, PostgreSQL, Python services, S3, Terraform, Prometheus, and Grafana.
Best for organizations building cloud systems that must remain secure, maintainable, and cost-effective over years of growth.
Regulated Systems and Engineering Leadership
- Healthcare analytics systems: Led platform and quality engineering in a regulated healthcare environment with direct responsibility for delivery and operational confidence.
- Clinical data and imaging platforms: Supported large-scale clinical data processing, DICOM metadata search, and long-term archive strategies in regulated enterprise settings.
- Data anonymization and compliance analysis: Helped define technical and regulatory requirements for secure, scalable clinical data sharing.
Best for teams where validation, traceability, and senior technical judgment are critical to the mission.