Revised resume points

content/resume/_index.md

@@ -11,12 +11,29 @@ draft: false
 languages="Python, SQL, Tableau"  
 date="July 2025" show="true" %}}
 
-* Built an end-to-end AML simulation using **Python**, generating **9M+ records** across customers, transactions, and alerts to mimic real-world financial behavior and suspicious activity patterns.
-* Wrote advanced **SQL (CTEs + joins)** to segment **high-risk customers**, calculate alert counts, and filter transactions over the past 90 days with aggregated exposure metrics.
-* Engineered a custom **risk scoring model** in Python using transaction thresholds and alert volume to classify customers as Elevated or Critical risk.
-* Designed **interactive Tableau dashboards** (Risk Heatmap, Alert Efficiency, Risk vs. Avg Amount) to visualize cross-country AML exposure and alert effectiveness.
-* Normalized multi-currency data and integrated key AML metrics like **alert rate (%)**, **avg USD amount**, and **transaction volume** for actionable reporting.
-- **Built KPI-ready metrics** (alert rate, avg USD exposure, transaction volume) to support AML performance reporting and enable cross-country risk comparisons.
+* Built an end-to-end AML simulation using **Python**, 
+  generating **9M+ records** across customers, 
+  transactions, and alerts to mimic real-world 
+  financial behavior and suspicious activity patterns.
+* Wrote advanced **SQL (CTEs + joins)** to classify 
+  **high-risk customers**, calculate alert counts, and 
+  filter transactions over the past 90 days with 
+  aggregated metrics.
+* Engineered a **risk scoring model** in Python 
+  using transaction thresholds and alert volume to 
+  classify customers as Elevated or Critical risk.
+* Designed **interactive Tableau dashboards** (Risk 
+  Heatmap, Alert Efficiency, Risk vs. Avg Amount) to 
+  visualize cross-country AML exposure and alert 
+  effectiveness.
+- **Developed KPI-ready metrics** (alert rate, avg USD 
+  exposure, transaction volume) to drive AML 
+  performance reporting and enable cross-country risk 
+  comparisons.
+- **Normalized multi-currency transaction data** to 
+  ensure consistent exposure calculations across USD, 
+  CAD, and EUR, supporting reliable AML metric 
+  aggregation.
 
 {{% /resume/project %}}
 
@@ -56,11 +73,11 @@ date="Oct 2022" show="true" %}}
   structured/unstructured metadata from APIs, enabling 
   real-time rarity scoring and improving insight 
   delivery by **80%**.
-- **Engineered a scalable data pipeline** in Django 
-  (Python) to ingest, process, and expose NFT ranking 
-  data via GraphQL, supporting **low-latency 
-  reporting** and scaling to **2,000+ concurrent 
-  queries** for end-user research.
+- **Optimized SQL query performance** within a 
+  Django-based pipeline, processing NFT ranking data at 
+  scale and exposing results via GraphQL with 
+  **low-latency response times under high concurrency 
+  (2,000+ queries)**.
 
 {{% /resume/project %}}
 
@@ -73,11 +90,28 @@ date="Oct 2022" show="true" %}}
 {{% resume/work-experience name="Red Hat" 
 title="Cloud/Software Engineer Intern" languages="Kubernetes, GoLang, Jenkins" date="May 2022 — Aug 2023" %}}
 
-* **Reduced reporting deployment time by 66%** by building a CLI-based solution to push compiled binaries directly into Kubernetes/Openshift clusters, accelerating turnaround for testing and data validation workflows.
-* **Decreased manual configuration errors by 80%** by automating service discovery and dynamic config updates, aligning with AML’s goal of minimizing operational risk and improving data integrity.
-* **Improved system reliability** during production launches by implementing startup probes for legacy services, reducing downtime and enhancing stability for automated monitoring/reporting pipelines.
-* **Enhanced CI pipeline reproducibility and performance** by rewriting the Jenkins nightly pipeline to support automated PR-level testing with reusable parameters, improving report consistency across environments.
-* **Collaborated cross-functionally** with developers and testers to maintain reliable infrastructure, echoing the AML role’s emphasis on stakeholder partnership for building robust reporting systems.
+- **Decreased manual configuration errors by 80%** by 
+  automating service discovery and dynamic config 
+  updates, aligning with AML goals of minimizing 
+  operational risk and improving data integrity.
+- **Enhanced CI pipeline reproducibility and 
+  performance** by rewriting the Jenkins nightly 
+  pipeline to support automated PR-level testing with 
+  reusable parameters, improving report consistency 
+  across environments.
+- **Collaborated cross-functionally** with developers 
+  and testers to maintain reliable infrastructure, 
+  echoing the AML role's emphasis on stakeholder 
+  partnership for building robust reporting systems.
+- **Improved system reliability** during production 
+  launches by implementing startup probes for legacy 
+  services, reducing downtime and enhancing stability 
+  for automated monitoring/reporting pipelines.
+- **Reduced reporting deployment time by 66%** by 
+  building a CLI-based solution to push compiled 
+  binaries directly into Kubernetes/Openshift clusters, 
+  accelerating turnaround for testing and data 
+  validation workflows.
 
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