My background spans enterprise solutions, analytics, machine learning, and stakeholder-facing engineering. I draw on those experiences to approach product strategy and architecture challenges with both business context and practical technical judgment.
My career includes work at Honeywell and Saviant Consulting, alongside subsequent senior analytics and product-company experience. These experiences taught me to translate business needs into technical execution across teams, systems, and stakeholders.
The earlier portfolio describes work on machine-learning and NLP models, automated reporting, AWS cloud deployments, and leading a four-person development team. Responsibilities also included candidate technical evaluations and KPI-driven alerting.
Worked directly with customers to gather business requirements, improve reporting and dashboard performance, and establish hierarchical access for stakeholders—an early foundation in understanding user needs and designing workable solutions.
Understand stakeholders, user needs, success metrics, and the priorities behind an MVP.
Translate business context into viable designs while evaluating integration, reliability, and scale.
Build iteratively, communicate trade-offs, and create solutions that help people make decisions.
SQL, data modeling, ETL, Tableau, Power BI, Qlik Sense, Python, and machine learning are part of my hands-on foundation. I'm building further expertise in cloud architecture and AI-enabled solutions, with professional implementations distinguished from experiments.
I'm focusing on product and solutions engineering opportunities in Boston, Massachusetts, and remote. I'm interested in the intersection of customer needs, enterprise technology, and practical AI adoption.