Luca
Luna

Lead technical direction across two teams — application and data platform (12 engineers) — for a data-driven automotive marketing platform built on Databricks, processing hundreds of millions of consumer records and serving as a core revenue driver for the business

Launched a rebuilt marketing campaign generation and audience segmentation product, replacing a multimillion-dollar annually licensed third-party tool with new capabilities that expand what sales and marketing teams can deliver

Architected unified data platform using Databricks Lakeflow pipelines with foreign catalog connections to Snowflake and external Databricks workspaces, delivering real-time ingestion with high-performance query capabilities across a nationwide consumer dataset

Reduced PII storage footprint by 97% across hundreds of millions of records through ephemeral storage, foreign catalog integration, and on-demand matching — lowering insurance and compliance exposure while preserving full analytical capabilities in Unity Catalog

Established comprehensive data governance framework with attribute-based access control (ABAC), automated lineage tracking, and dynamic data masking policies in Unity Catalog, ensuring CCPA/GDPR compliance while enabling self-service analytics for sales and marketing teams

Developed consumer intention-scoring algorithms analyzing ownership history, demographics, in-market propensity, economic cohorts, and buying power, enriched with dealership-specific sales data to power conquest advertising and high-intent buyer identification

Built event-driven microservices on AWS with CI/CD automation for both application and Databricks deployments, implementing Edge Delta observability that accelerated release cadence from monthly to daily

Present platform architecture, product demos, and technical roadmaps to executive leadership, and deliver enablement sessions for sales and marketing teams on self-service analytics

Lead developer on ATSC 3.0 emergency alert platform integrated with FEMA IPAWS, owning architecture, implementation, and feature delivery through beta launch to broadcast stations

Developed a hyper-targeted alerting system with geographic precision at DMA, FIPS, zip code, and custom polygon levels, enhancing message delivery precision

Designed system architecture using Python, AWS, Terraform, and DynamoDB to ensure scalability and reliability

Collaborated with lawmakers and broadcasters to promote broader adoption of advanced alerting technologies

Contributed to ATSC 3.0 emergency alert system development, focusing on core platform features and reliability

Implemented AWS microservices architecture for critical alert delivery infrastructure

Collaborated with cross-functional teams to support beta deployment to broadcast stations

Developed RAG-based AI chatbot prototypes using Claude API and AWS Titan embeddings for state agency knowledge management, alongside PowerShell automation tools for data processing and reporting

Built automated testing infrastructure using Playwright for $1B+ state grants platform (Intelligrants IGX), creating test suites that reduced regression testing time and improved release reliability

Contributed to monolith-to-microservices migration moving on-premises manufacturing system to cloud architecture and built ETL pipeline extracting legacy data from Epicor ERP to modern web application, improving system deployment flexibility and data accessibility

Mentored 1200+ undergraduate students across multiple semesters in C systems programming and operating systems fundamentals including pointer arithmetic, dynamic memory allocation, process control, signal handling, and inter-process communication, developing hands-on debugging workshops and comprehensive code review sessions that strengthened student understanding of low-level systems architecture and software engineering best practices