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Peter Ingulli

@peteringulli1

Staff software engineer building scalable, low-latency distributed systems.

United States
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What I'm looking for

I’m looking to architect and modernize distributed systems end-to-end—shipping reliable automation at scale, strengthening observability and security, and mentoring engineers while delivering measurable business impact.

I’m a Staff Software Engineer focused on architecting end-to-end automation and distributed systems that measurably improve operational outcomes. At Workday, I’ve helped cut enterprise Financial Close cycles by 50% (15 to 7 days) through reconciliation workflows, real-time reporting, and exception handling.

I build predictive and anomaly detection systems that compress multi-day analysis into hours, improving forecast accuracy for Illuminate and Financial Management. I also lead AP/AR modernization that eliminated weeks of manual invoice processing, accelerated cash flow, and prevented overpayments at scale.

My approach blends engineering depth with strong delivery practices: I’ve engineered Java/Go/Python backend services with Kafka streaming, created AI-driven financial dashboards, and established observability with Prometheus and Grafana to lower incident resolution time by 40%. I modernize CI/CD using Jenkins and Playwright, and I mentor 7 engineers to raise architectural standards and increase sprint velocity by 30%.

Experience

Work history, roles, and key accomplishments

Workday logoWO
Current

Staff Software Engineer

Jul 2019 - Present (6 years 9 months)

Architected end-to-end automation for Financial Close workflows, cutting close cycles from 15 to 7 days (50% reduction) for enterprise customers. Built ML-driven forecasting and anomaly detection, modernized AP/AR to eliminate weeks of manual processing, and improved incident resolution time by 40% through Prometheus/Grafana observability.

General Electric logoGE

Senior Software Engineer

General Electric

Jul 2012 - Aug 2019 (7 years 1 month)

Delivered real-time hospital operations software using streaming and predictive ML, decreasing length of stay by 1.07 days and reducing emergency department stays by 41% within 7 months. Built Kafka/Redis pipelines to reduce bed assignment latency by 66%, integrated Amazon SageMaker to lower temporary staffing expenses by 50%, and improved industrial turbine performance with predictive maintenance

Education

Degrees, certifications, and relevant coursework

Rensselaer Polytechnic Institute logoRI

Rensselaer Polytechnic Institute

Bachelor of Science in Computer Science, Computer Science

2007 - 2011

Grade: GPA: 3.9

Earned a Bachelor of Science in Computer Science (2007–2011) at Rensselaer Polytechnic Institute with a GPA of 3.9, with coursework including data structures and algorithms, principles of software design, probability & statistics, and linear algebra.

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