Priya Nayak

SWE @ DoorDash

Seattle, Washington, United States

About

Experience

  • Software Engineer at DoorDash
    Jun 2023 - Present · 3 yrs 1 mo

  • Business Data Analyst at USC Auxiliary Services
    Sep 2021 - Apr 2023 · 1 yr 8 mos

    Business Data Operations division of USC Hospitality

  • Software Engineer at DoorDash
    May 2022 - Aug 2022 · 4 mos

    • Prompted an increase in the convert-to-pay rate of DashPass users by 6% by improving the functionalities of the DashPass feature. • Single-handedly planned & built enhancements to the Kotlin code base, using gRPC for communication across services, GraphQL for schema creation & API querying, with database connectivity for CockroachDB & PostgreSQL. • Ensured test-driven development by writing unit & functional test cases in Kotlin for the feature enhancements.

  • SAP Ariba (2 yrs 2 mos)
    • Software Engineer
      Jun 2019 - Jul 2021 · 2 yrs 2 mos

      • Enhanced the customer experience for the cloud-based product SAP Ariba by designing its automatic integration & onboarding process with SAP Analytics Cloud. • Collaborated with cross-functional Agile team to gather requirements, translate those from PRDs into project parameters in JIRA, and make sound technical decisions that ensure fault tolerance & high availability. • Built SQL queries for SAP Analytics Cloud using Apache Kafka for asynchronously dealing with data processing & querying. • Designed REST APIs with Java Spring Boot for Microservices architecture, using JPA for data interaction with OracleDB & SAP HANA DB and JUnit tests for unit testing. • Monitored app performance, troubleshooted & debugged performance bottlenecks using Splunk & Jenkins.

    • Contributor - Diversity & Inclusion Team
      Dec 2019 - Feb 2021 · 1 yr 3 mos

      Event co-ordinator for SAP Early Talent Summit 2020 & Early Talent Kick-off Editor & Content writer for SAP Ariba Diversity & Inclusion Newsletters

  • Software Engineer at SAMSUNG R&D INSTITUTE INDIA - BANGALORE PRIVATE LIMITED
    May 2018 - Jul 2018 · 3 mos

    • Conducted research studies to explore techniques for building a goal classification model with limited training data, including Transfer Learning, Active Learning, One-Shot Learning, and Few-Shot Learning techniques. • Implemented two distinct models CNN, & SVM, with Active Learning and conducted comparative analysis on both.