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Priya Nair

Backend Engineer

priya.nair@email.com(408) 555-0193Seattle, WAgithub.com/priyanair

Summary

Backend engineer with five years building distributed systems, most recently focused on payments infrastructure handling millions of transactions a day.

Skills

GoPythonPostgreSQLKafkaKubernetesAWSgRPCSystem designRustRedisTerraformOpenTelemetryDistributed systemsPostgres tuning

Experience

Backend Engineer @ Ledgerline

2022Present

  • Redesigned the transaction service to handle 3x throughput without added infrastructure cost
  • Cut p99 latency on the payments API from 800ms to 210ms
  • Introduced contract testing between six internal services, catching breaking changes before deploy
  • Designed an idempotent ledger-write path that removed duplicate postings under retry storms
  • Cut p99 API latency from 480 ms to 140 ms by introducing read replicas and query caching

Software Engineer @ Fieldnote

20192022

  • Built the event ingestion pipeline processing 50M+ events/day
  • Built a feature-flag service used by 15 teams to ship changes safely to production
  • Reduced infrastructure cost by 28% by right-sizing services and adopting spot instances

Software Engineer Intern @ Northstar Robotics

20172017

  • Implemented a telemetry ingestion service in Go handling 20k messages per second in testing
  • Wrote integration tests that caught two protocol-version bugs before the fleet rollout

Education

B.S. Computer Science, University of Washington

2019

Projects

  • queuelite — An open-source, embeddable job queue for Go services — 600+ GitHub stars

Certifications

  • Certified Kubernetes Application Developer, CNCF, (2022)

Volunteer Experience

  • Mentor — Code2040 — Meet monthly with early-career engineers on system design and interviewing.

Publications

  • Exactly-once semantics in payment pipelines, Ledgerline Engineering Blog, (2023)

Backend Engineer Resume Example

Based on Priya Nair’s resume, built with the Technical template

A skills-forward layout that reads like an engineer's own tech stack.

Why this works

  • Skills appear as individual tags right under the summary, the way engineers actually think about a stack
  • Bullets lead with concrete technical metrics — latency, throughput — instead of vague ownership language
  • A GitHub link sits next to contact info, where a technical reviewer expects to find it
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