At a Glance

• Built a governed data platform (API + Explorer) to make plant process data accessible and reliable across teams.
• Connected technical delivery with business adoption across manufacturing, quality, and enterprise analytics stakeholders.
• Created reusable infrastructure that supported later AI and GenAI initiatives.

Challenge

Process data existed across multiple systems, with inconsistent quality checks, access patterns, and reporting conventions. That fragmentation slowed troubleshooting, made benchmarking harder, and limited confidence in advanced analytics.

What I Led

• Architecture and delivery of the EDA DataSet API and Explorer App for governed self-service analytics access.
• Cross-functional alignment with Corporate IT, data engineering, and plant teams on security, lineage, and usability.
• Implementation of ingestion and validation patterns that improved consistency of downstream metrics and analyses.
• Operational rollout support so teams could adopt the platform in daily root-cause and performance workflows.

Business Impact

• Improved decision speed by reducing friction between data request and actionable analysis.
• Enabled more standardized reporting and performance benchmarking across North American sites.
• Strengthened the foundation for quality, Voice of the Customer, and AI-enabled initiatives.

How It Was Built

• Cloud and analytics stack: Azure ML, Snowflake/Snowpark, and Python-based APIs.
• MLOps and governance emphasis: data quality checks, lineage visibility, and repeatable deployment practices.
• Delivery model: iterative releases with stakeholder feedback loops from plant and enterprise teams.

Why It Matters for Future Teams

This work demonstrates a repeatable pattern for translating complex enterprise data environments into practical, adoption-ready systems. For future teams, the key takeaway is not just technical build quality—it is the ability to tie architecture, governance, and day-to-day user outcomes into one coherent operating model.