Resume

Experience

(Scottsdale, AZ) June 2020 – Present

Manage Starbucks’s Security Orchestration, Automation, and Response (SOAR) platform. Integrate with SIEM and other security consoles to capture hundreds of notable events and incidents daily. Build playbooks to collect supporting evidence, extract indicators of compromise (IOCs), and automate security responses.
Operate cybersecurity asset management. Identify infrastructure across multiple different inventory and security agent tools to assess security gaps and build supporting enforcements and automations to remediate security gaps.
Automate vulnerability management – Automation and data pipelines to handle tagging and due date assignments of more than 1 million vulnerabilities per day. Utilize tags to build robust RBAC for self managed patching across Starbucks Technology teams.
Maintain scanning infrastructure – Build global vulnerability scanning infrastructure across on-prem data centers, Azure, manufacturing plants, and regional offices across North America, EMEA, and APAC.
User and entity behavior analytics (UEBA) – Maintain data pipelines that feed millions of logs into UEBA platform to assess internal risk daily. Build supporting automation/playbooks for case management,

(Scottsdale, AZ) Aug. 2018 – May 2020

Built security orchestration to automate mobile device management (MDM) deployments of apps and surveys North American retail devices, reducing deployment process time by more than 75%.
Created reporting scripts and visualizations for team and upper management to drive security compliance within the organization.
Implemented continuous integration and continuous deployment (CI/CD) with Jenkins into team’s development process.

(Bentonville, AR) May 2018 – Aug. 2018

Developed internal application for the Payment Acceptance Team to audit how Walmart handles payment card transactions.
Used internal REST APIs to determine the attributes associated with issuer/bank identification numbers that Walmart supports.
Enabled business partners to view and update transaction rules associated to more than 10,000 different types of credit cards.
Refactored legacy point of sale troubleshooting tools.
Technologies: React, Node.js, JavaScript, HTML, CSS, git

(Tempe, AZ) Jan. 2017 – May. 2019

Assisted Professor in conveying object-oriented programming and data structures concepts to students.
Held office hours, hosted review sessions with 20+ attendees, and served as a bridge between students and Professor.
Technologies: Java

(Phoenix, AZ) May 2017 – Dev. 2017

Taught students, grades K-2 and 6-8, principles in Engineering, Science, and Technology through hands-on and project-based learning with LEGO.
Instructed after-school enrichment programs for 20 at-risk middle school students.
Organized summer camps with class sizes of 12-15 students.

Spring 2020

Genetic Algorithm optimization implemented to counter a given team composition given 552,689,424 total team compositions.
Application allows users to generate team compositions to determine best counter matchups using Riot Games API matchmaking data.
Visualization to show optimized team performance.
Azure app service hosting with GitHub workflows.
Technologies: React, JavaScript, Python, Azure
https://github.com/brycepedroza/league_gen_alg

Spring 2020

Leveraged Python’s Tweepy library to stream geo-tagged Tweets and observe if Twitter can be a substitute for calculating thermal comfort.
Logistic regression model utilized to classify Tweets as related to weather or not.
Built and trained a Naïve Bayes classification model to predict if a tweet had a positive or negative sentiment.
Application built to map weather related tweets and their associated sentiment and visualization built to calculate general trends over a given area.
Azure app service hosting with GitHub workflows.
Technologies: React, JavaScript, Python (Fast API), Azure (App service, Cosmos DB)
https://github.com/brycepedroza/weather_tracker

Fall 2019

Data-lake POC implemented to restrict or allow data access within the Hadoop Distributed File System (HDFS) depending on roles and additional attribute-based policies of a given user with granularity at the key: value level.
Policy ingestion done at the data stream layer and MapReduce layer to allow for more customized and granular form of access control within HDFS with minimal overhead.
Technologies: Apache Hadoop (HDFS & MapReduce), Apache Kafka, Apache Hive, Docker, Python (Faust), Java
https://github.com/brycepedroza/docker-hadoop-python