career

The timeline.

From process automation to platform ownership.

2018–2020

Customer Service Quality Manager

Amazon

Defined annual quality strategy for operations serving 10M+ contacts. Built QuickSight analytical dashboards. Automated recurring workflows using Python, SQL, and VBA macros—saving 40 hours/week. Improved CSAT by 250 basis points.

10M+ annual contacts QuickSight dashboards Python/SQL/VBA automation +250bp CSAT improvement
2020–2022

Service Delivery Leader, Screening Services

Amazon

Led cross-functional policy revamp for India background check process, impacting 120K annual hires. Reduced turnaround from 4 months to 1 month (75% improvement). Scaled operational team from 10 to 45 FTEs. Mentored 3 managers and 3 specialists.

120K annual hires impacted 4mo → 1mo turnaround 10 → 45 FTEs Mentored 6 leaders
2023–Present

Global Payroll Payments Manager

Amazon

Led end-to-end deployment of in-house payroll platform across 6 APAC markets processing $1.2B annually. Defined technical requirements for 2 engineering teams. Coordinated payments via API/SFTP with ISO 20022 specs. Drove fraud detection initiative analyzing 1.5M transactions. Reduced manual validation by 80% through automation.

$1.2B annual payroll 6 APAC markets 1.5M fraud analysis 80% automation gain

The first lesson came from customer service: data beats opinion every time. If you can measure it, stop guessing. Building QuickSight dashboards that turned raw numbers into actual decisions — that was the moment I realized every argument just ends when the data loads.

The background check revamp taught me that you can't engineer your way out of a problem you don't understand. It wasn't about code. It was about sitting inside the process, getting Legal and Compliance on the same page, and earning trust that the new rules were actually safe. You have to understand the system before you can fix it.

Automation is tempting, but the question isn't "can this be automated?" — it's "should it be?" Python scripts saved 40 hours a week in the early days. The fraud detection engine reduced manual validation by 80%. But the remaining 20% — the edge cases that need judgment — that's where the real work is. Automate the obvious, protect the nuanced.

The homelab isn't a side project. It's where the professional and personal blur together — 49 containers on bare metal, no cloud provider, full observability. There's a specific satisfaction in knowing exactly where your things live and why they work. Own your infrastructure.


Tools & Domains

PythonSQLQuickSightVBADockerLinuxKubernetesGrafanaPrometheusLokiCaddyMatrix
ISO 20022
PaymentsCompliance EngineeringProduct Management

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