Ryan Timoney

Frankly Fiscal is a SaaS that provides users a consolidated view of their stock investments held in all major banks and brokerage firms. We provide clear context and personalized investment insights.

As the founder, I’m responsible for all architectural decisions and engineering.

Key activities:

• Architect, construct and manage lightweight, scalable, highly-available cloud infrastructure for big data processes on a strict budget.
• Learn quickly to identify and resolve novel technical challenges across many technologies and cloud/data/service providers.

Example work:

• Automatically ingest a data feed from multiple stock exchanges to summarize activities of thousands of tradable instruments (such as stocks) daily. We’re currently using ~24 million rows of data for over 10,000 tradable equities and ETFs. Tools in use include Python, Terraform, Amazon Web Services Lambda, Polars (similar to Pandas), PostgreSQL, and ClickHouse (a columnar database provider).
• Programmatically find, obtain, extract and store corporate and market data from public data sources in arbitrary arrangements and formats. Tools in use include Python, Claude (Anthropic’s Large Language Model), Llama 3 (Meta’s LLM), ChatGPT (OpenAI’s LLM), Scrapy, Selenium, PostgreSQL, Amazon Web Services S3 and proxy providers.
• Design, develop and deploy a SaaS front-end using Next.JS, TypeScript and Vercel.
• Set project goals, creating, prioritizing and managing Kanban tickets.
• Minimize costs across 20+ cloud/data/service providers. Some of these are Amazon Web Services, Google Cloud Platform, Vercel, Neon, ClickHouse, Anthropic, OpenAI, GitHub, and Paddle.

CTC is a privately held, world-class equities (stocks) and derivatives (options, futures, etc.) brokerage firm operating in stock exchanges in New York City, Chicago, and London. Their DevOps team owns the cloud infrastructure, and my work was primarily focused on the cloud infrastructure.

I had broad infrastructure-related responsibilities. These included spearheading the development of Amazon Web Services infrastructure; designing and implementing a firm-wide (on-prem and multicloud) secrets management system; building novel infrastructure to support aggressive, big data projects for quant teams (similar to data scientists in operations); implementing the first Large Language Models for broad application to in-house datasets; and more.

Example Roles/Projects:

Lead Amazon Web Services Platform Design, Development and Maintenance

Architect and build out Amazon Web Services accounts, VPCs (networks), IAM Roles (permissions and security), the Elastic Kubernetes Service (EKS) clusters, S3 (cloud storage) buckets and other resources. All of this was done using Terraform (Infrastructure as Code), enabling us to scale quickly and transparency.

Lead Elastic Kubernetes Service (Kubernetes) Cluster Development

Business needs required clusters on AWS. As Red Hat OpenShift clusters were running on-prem, I was tasked with deploying a parallel to these on AWS (ROSA clusters) and resolving all prerequisites (VPCs, IAM, etc). This was successfully constructed on time. After discussions, demonstrations and assessing cost/metrics, I led a migration from ROSA to EKS. I built them with Terraform and established the Standard Operating Procedures for building additional clusters. These were improved as new business/project needs arose. I generated extensive documentation for the systems and assisted in migrating dozens of applications to Elastic Kubernetes Service.

Bayer Crop Science is one of the world’s largest and most advanced agriculture solutions providers. They make extensive use of Amazon Web Services (AWS), Google Cloud Platform (GCP) and Azure and were, in 2020, running the largest Kubernetes cluster on earth.

My work there included platform engineering on Amazon Web Services and Google Cloud Platform, and all DevOps practices such as CI/CD, cross-team coordination, and more.

Example Roles/Projects:

Lead Google Cloud Platform Engineer

New business needs required the development of resources on GCP, and I was brought on board to engineer those. I designed, constructed, and managed the entirety of our division’s GCP infrastructure. This included the VPCs (networking), virtual machines, GKE (Kubernetes) clusters, IAM groups/roles/permissions (security), Service Accounts, CloudDNS, Cloud Functions, Pub/Sub, Identity Aware Proxies, Key Management Service, Google Cloud Storage, BigQuery data warehouses, and more.

Lead Google Kubernetes Engine (GKE) Engineer

Developed a Terraform module for N number of private GKE clusters. Once the clusters were up, Kustomize-constructed manifests for ArgoCD, AWS Pod Identity, Cert Manager, Prometheus, SealedSecrets, and various custom platform services were applied.

Extend, Support and Maintain CI/CD

Our team owned the CI/CD pipeline. ArgoCD and Jenkins were the primary tools. GitHub was used to store the repositories and as an Identity Provider. Applications were deployed automatically after appropriate users/groups approved PRs. Code Owners files were used to designate relevant parties at the directory level.