Work History

• Evaluated generative AI, intelligent automation, and knowledge-assisted workflows for healthcare data engineering and operational use cases.

• Assessed internal LLM-based tools and agentic workflows for reliability, workflow effectiveness, implementation constraints, and operational suitability before broader adoption.

• Supported Client Data Engineering teams with healthcare data integration, interoperable pipeline design, reusable implementation patterns, and automated technical documentation.

• Collaborated with product, engineering, and delivery teams to validate emerging capabilities against real implementation requirements and incorporate user feedback into solution design.

Architected and led a domain-by-domain migration from legacy data systems to a modern data platform using the Strangler pattern, enabling incremental modernization while maintaining continuity for existing client workflows.

• Engineered an HL7 FHIR integration framework that decoupled healthcare transformation logic from source-specific schemas, improving reuse across client integrations.

• Automated pipeline documentation, DAG visualization, and data-lineage generation to improve visibility into dataset dependencies, transformation stages, and downstream flows.

• Designed fail-safe data pipelines using Audit Balance Reports and runtime observability metrics to validate ingestion completeness and identify processing discrepancies.

• Developed reusable engineering patterns for healthcare interoperability, pipeline validation, and technical documentation across implementation workflows.

• Mentored junior engineers and delivered technical training sessions to improve knowledge sharing, onboarding, and consistency across engineering practices.

Designed scalable data pipelines in Databricks, leading end-to-end ingestion of complex sources like the Salesforce Health Cloud Dataset.

• Orchestrated batch-processing workflows and performed performance profiling, query optimization, and execution-plan tuning to improve data-processing efficiency.

• Automated Bronze-to-Silver data-mapping workflows to transform client-specific healthcare data into standardized downstream structures.

• Built stored procedures, UDFs, and modular SQL components to encapsulate reusable healthcare transformation and validation logic.

• Transformed raw staging datasets into structured, analytics-ready tables for downstream reporting and data-processing workloads.

• Developed automation utilities for repetitive analysis, validation, and debugging tasks, reducing manual engineering effort across recurring pipeline workflows.

Completed the Data Engineering Fundamentals

• Completed a Data Engineering Fundamentals program covering database design, ETL architecture, data transformation, and data-warehousing concepts.

• Built Python and Apps Script automation tools to streamline recurring data-entry, reporting, and internal operational workflows.