Case Study: EDA Data Platform
Operationalizing governed plant data for enterprise analytics and decision velocity.
Project Snapshot
- Role: Lead Data Scientist / Platform Architect
- Domain: Manufacturing analytics and governance
- Stack: Azure ML, Snowflake/Snowpark, Python APIs, MLOps
- Timeline: 2022 – Oct 2025 (enterprise delivery phase)
18-month program duration
16+ Hours/Week Reclaimed
Program-level analytics tooling reduced recurring engineering and administrative effort by more than 16 hours per week (public-shareable resume metric).
Targeted quality workflows
5% → 1% Defect Drift
Data-informed quality programs reduced nuisance defect rates from 5% to 1% in targeted manufacturing workflows (program-level outcome).
One-year optimization window
>10% Yield Lift
Root-cause and process optimization efforts supported by governed analytics delivered more than 10% yield improvement in a one-year window.
Technical Architecture
graph TD
subgraph Sources
A[Plant Sensors] --> B[ historians ]
C[ERP Systems] --> D[SAP/Oracle]
E[Quality Labs] --> F[LIMS]
end
subgraph Ingestion
B --> G[Ingestion Pipeline]
D --> G
F --> G
G --> H[Validation Layer]
end
subgraph Storage
H --> I[Snowflake Data Warehouse]
end
subgraph Delivery
I --> J[REST API]
I --> K[Dashboards]
I --> L[ML Models]
end
subgraph Consumers
J --> M[Plant Engineers]
K --> N[Operations Teams]
L --> O[Data Scientists]
end
Data flow: Plant sensors, ERP systems, and quality labs feed into a unified ingestion pipeline. Validation ensures data quality before storage in Snowflake. Delivery layers include REST APIs, dashboards, and ML model endpoints.
Decision Tradeoffs
| Option Considered | Pros | Cons | Decision |
|---|---|---|---|
| Snowflake-native | Managed infrastructure, fast queries, Snowpark for transformations | Vendor lock-in, per-query pricing at scale | Selected — enterprise already invested, team expertise available |
| PostgreSQL + PostGIS | Open source, full control, no query pricing | More ops overhead, team capacity constraints | Rejected — would require dedicated DBA capacity |
| Databricks Unity Catalog | Strong ML integration, governance features | Higher complexity, migration cost | Deferred — considered for future ML platform consolidation |
Quantified Outcomes (Public-Shareable)
- 16+ hours/week of engineering and administrative effort reclaimed through analytics automation patterns.
- 5% to 1% nuisance defect-rate shift in targeted quality workflows using stronger data feedback loops.
- >10% yield improvement delivered in a one-year optimization window where governed analytics informed interventions.
Problem
Manufacturing stakeholders needed reliable, timely, and consistent access to process data, but data was fragmented across systems and teams. This slowed troubleshooting, benchmarking, and adoption of advanced analytics.
Approach
I led design and deployment of a governed EDA platform composed of ingestion pipelines, validation rules, and API-based delivery. The architecture balanced plant usability, IT governance, and analytical flexibility.
Outcome
The platform became a core analytics layer for multiple initiatives, enabling faster root-cause analysis and more consistent reporting across operations. It also created the technical baseline for next-gen applied AI use cases and informs my post-Oct 2025 focus on agentic workflows and production programming systems.
Leadership Contribution
- Architecture: Designed the data model, ingestion pipeline, and validation layer — decided on Snowflake-native approach after evaluating PostgreSQL and Databricks options.
- Team: Led 3-person analytics team through delivery, establishing code review and testing practices.
- Governance: Established data quality standards that were adopted plant-wide, including validation rules and documentation.
- Outcomes: Measured and reported ROI to leadership quarterly, translating technical metrics into business impact.