<aside> 🚀 Data Platform Engineer → AI & Data Product Manager
I turn complex data architectures into measurable product decisions. 12+ years designing enterprise data platforms (Azure, Spark, lakehouse) across banking, manufacturing, FMCG, and pharma — now applying that systems depth to AI product strategy, KPI design, and fairness-first ML.
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| Dimension | How I Apply It |
|---|---|
| Problem Definition | I start with the decision the user is trying to make — not the data they're asking for. A plant manager requesting "real-time data" actually needs same-day anomaly visibility. Reframing changes the entire scope. |
| KPI Thinking | Metrics should expose trade-offs, not hide them. I designed 6 evaluation metrics across 3 categories (relevance, ranking quality, business yield) — because a single metric always tells a partial story. |
| Trade-off Analysis | Every system has a cost-accuracy-speed triangle. Chose TF-IDF over transformer embeddings — lower accuracy ceiling, but 10x faster iteration and fully explainable to non-technical stakeholders. That's a product decision. |
| Prioritization | Deprioritized a large-scale historical backfill (requested by analytics) to stabilize daily pipelines for 3 business units. Communicated the trade-off, delivered the backfill as a phased rollout the following quarter. |
When you read them, look for:
I don't build features. I build systems that make better decisions.
<aside> 📬 Open to Technical Product Manager (AI & Data Platform) and Data Product Manager roles
12+ years building data platforms at IBM, Capgemini, and Cognizant. Now applying that depth to product, with 7 self-directed case studies covering AI credit decisioning, regulatory compliance, recommendation systems, and data quality.
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