• Building out Proof of Stake validators and full nodes for various blockchains using automated DevOps solutions• Galaxy (TSX: GLXY) is a technology-driven financial services and investment management firm that provides institutions and direct clients with a full suite of financial solutions spanning the digital assets ecosystem. • Galaxy operates five synergistic business lines: Trading, Asset Management, Principal Investments, Investment Banking, and Mining.• Galaxy’s CEO and Founder is Mike Novogratz. The Company is headquartered in New York City, with offices in Chicago, San Francisco, London, Amsterdam, Tokyo, Hong Kong, the Cayman Islands (registered office), and New Jersey. Additional information about the Company's businesses and products is available on www.galaxy.com

• Built and managed AWS infrastructure using Terraform and Ansible• Worked closely with smart contract engineers to build out POCs with various blockchains including Avalanche and Horizen Labs• Closely coordinated with the security team and SOC analysts in monitoring and identifying malicious web traffic to our APIs and web app• Enabled the new WAF Bot Control feature on our external API which provides a detailed dashboard that gives visibility and control over common bot traffic such as scrapers, scanners and crawlers• Created a Slack channel for monitoring all autoscaling events from EC2 instances so the team was alerted about any production downtime

• Founder and lead developer of William Campbell Consulting Corp. (WCCC) which focuses on providing Data Engineering, DevOps and Infrastructure as Code services to its clients including Harry’s Inc. and Hewlett-Packard Enterprises• WCCC is a registered S Corporation in the state of New York

• Established a DevOps culture within the data engineering team by implementing an automated CI/CD pipeline with Infrastructure as Code using Terraform and Bitbucket to enable highly reliable, scalable and secure cloud solutions in a multi-account AWS environment• Reduced the company’s total AWS bill by 60% by configuring auto scaling policies and spot instances for business applications running on ECS Fargate• Designed and built an end-to-end disaster recovery solution for all data and applications hosted in AWS which included cross-region replication of data in S3 as well as automated Lambda & ECS application failover that was in compliance with SOC2 standards• Spearheaded the release management process for 10+ frontend and backend repositories utilizing Jira and Bitbucket deployment best practices including fix version assignment and automated release notes creation while engaging in a true agile software development lifecycle• Coordinated with the cloud security team in securing sensitive PII data in S3 and following the principle of least privilege while provisioning the data engineering team access to various AWS resources via secure IAM roles and policies• Developed a user-friendly pipeline management solution for the underwriting team in Monday.com that tracked insurance policies at each stage of their lifecycle, combining an automated data feed from Gallagher’s SmartMarket tool with manual data collected from broker touchpointsNote: Blackboard Insurance was shutdown on Aug 31st, 2020 due to financial impact of COVID-19 on parent company AIG, resulting in 100% of employees being laid off.

• Consultant for Caserta - an innovative data warehousing and analytics company specializing in helping data-driven businesses upgrade their IT infrastructure to modern day cloud-based systems including Google Cloud and AWS to support the foundation for machine learning and AIPast and current projects include:1) New York Times (NYT) : January - April 2018New York, NY•Collaborated with The New York Times data engineering team in transferring their entire Print Data Warehouse and reporting system to the Google Cloud Platform, built entirely using Python, Spark, Airflow, BigQuery, Dataproc, Datalab, Docker, Cloud Storage and Chartio2) SoulCycle : April - July 2018New York, NY• Brought in to help SoulCycle transfer their entire analytics stack to Google Cloud using tools including Spark Streaming, Jupyter notebooks, Airflow, Google PubSub, Cloud Storage, DataProc, BigQuery and Looker• Responsible for building the ETL scripts that ingested raw data from the source system and applied various transformations defined by a dimensional model in order to make the data more structured and ready for analytics use cases3) Canadian Pension Plan Investment Board (CPPIB) : July - September 2018Toronto, ON• Traveled onsite to Toronto, Canada to scope out the project of transferring CPPIB’s entire legacy data analytics system to AWS• Constructed an initial POC with Redshift, Snowflake, and Airflow to prove that our analytics solution was able to scale to support BI tools like Tableau4) WWE : December 2018 - April 2019Stamford, CT• Built out a hybrid-cloud data lake/ data warehouse solution on Google BigQuery/ AWS Redshift using S3, Cloud Storage, Cloud Composer, Dataproc and Spark containing multiple structured layers of Google Analytics data

• Chosen by instructors to be a part-time teaching assistant responsible for offering extra help to students enrolled in the Data Science Immersive course• Topics include: Python, SQL, machine learning, data mining, feature engineering, data visualization, web scraping, natural language processing, deep learning, time series modeling, relational databases

• Engaged in a 12-week data science career accelerator focused on supervised classification and regression modeling, unsupervised clustering, visual and statistical analysis, SQL databases and big data distributed systemsCapstone Project: Implemented a gradient boosted decision tree model to predict NYC taxi trip times in seconds using latitude and longitude coordinates from over 1.5 million yellow cab rides, resulting in a top 9% finish on KaggleProject: Applied a regularized linear regression model to predict house prices in Ames, Iowa using features such as lot area, overall quality, year built and house style which assessed the price and dollar value of renovations to a homeProject: Acquired data through web scraping IMDb.com and the TV Maze API to analyze characteristics that make up the all-time greatest TV shows and built a random forest classifier predicting whether or not a TV show is in the top 250 of all-time greatest TV shows