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Manulife

Software Engineering Intern

Jan 2026 - Apr 2026

At Manulife, I built data and telemetry pipelines that helped surface resource bottlenecks before service degradation. The demo below is a simplified version of that early-warning dashboard.

Early warnings from telemetry

Illustrative demo
Early warnings from telemetry controls
QUEUE DEPTHEXAMPLE THRESHOLD: 150
Queued jobs
42
Memory
48%
Data age
4s

No example thresholds crossed

The example readings are within the demo’s configured limits.

Synthetic telemetry and demonstration thresholds. No live company systems are connected.

What I worked on

Processed over 500,000 Salesforce records per export by building a scalable Python pipeline with Redis caching to extract, transform, and adapt application data into downstream-compatible formats.

Reduced system downtime risk by building a New Relic telemetry exporter and SQL-based monitoring pipeline, enabling early detection of more than three resource bottlenecks before service degradation.