Pysynapse

Selected Outcomes

Data Engineering Accomplishments

These examples highlight the kinds of business-critical problems I solve: unreliable pipelines, fragile reporting, insecure data movement, and cloud systems that need stronger governance without slowing delivery.

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.

Analytics Modernization

  • 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.
  • Practical modernization path: Replaced ad hoc reporting patterns with cloud-native ingestion, transformation, and observability layers without requiring a risky big-bang cutover.

Cloud Architecture and Secure Data Exchange

  • Hybrid SFTP to cloud analytics gateway: Connected AWS-based SFTP silos with GCP-native analytics to create secure, automated, and auditable feed handoff.
  • Cloud-native ingestion: Built Gen2 Cloud Functions based ingestion services that handled bursty workloads while enforcing validation and schema controls.
  • 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.

Regulated Systems and Long-Tenure 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.
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