Technical Writer II

I worked at Amazon as a Technical Writer, but I did so much more than simply write. My primary focus is towards developing team and organization mechanisms for economists, engineers, and scientists. I created all forms of content: Wikis, Broadcast Videos, API documentation, Runbooks, PR/FAQs, and even presented online workshops. I’m especially proud of the scope of impact in my work.

Professional Summary

  • Hired into an Engineering Team of Software Developers and Data Engineers.
  • Collaborated with multiple teams on Econometrics and Infrastructure.
  • Customer: Internal Tiger Teams, entire org of Core AI (500+).
  • Develop code for the Sphinx Framework (reST) for docs as code.
  • Documented AWS Services, Data Policies, and API Software Documentation.
  • Create Systems Diagrams of AWS Architecture using Lucid Chart.
  • Owned Broadcast Channels and Videos, including editing highly technical demos and non-technical How tos.
  • Led workshops for mechanisms, tenets, and product improvements.
  • Maintained Knowledge Portals for customers.
  • Met with Product Managers to align documentation with product roadmap.
  • Document critical third party software
    • VSCode integration with internal product and Amazon’s Extention marketplace.
    • Amazon’s internal version of Git for version control and code review.
    • Amazon’s Internal Agile PM software for Agile Scrum and Kanban.
  • Bonus: Scripted a Intake Form Automation using JavaScript.
  • Work with Administrative leaders to create a wiki for DEI programs in our Org.

Content Creation

Technical Writing

The main type of content I worked on, ordered by frequency.

  • Software Documentation: Documentation for devs and eng teams for APIs and SDKs.
  • How Tos: A step by step guide to accomplish one high-level objective.
  • Tutorials: Focused content with an emphasis on a use case and end result.
  • Product Launch Emails: To the point information with a template for releases.
  • Issue Tickets: Structured Content to be parsed into props/ticket variables.
  • Knowledge Hubs: Resource page with links and descriptions of more links.
  • Schematics: Diagrams and charts for highly-technical low-level relationships.
  • PR/FAQS: Customer facing documents for a senior executive proposal.
  • Runbooks: Code Snippets and docs for internal contribution or demos.
  • Posters: Detailed Poster for Summit presentation focusing on dev mechanisms.

Out-of-scope

Honorable mention of “volunteer” work with lower priority, but good for learning and career paths as a Technical Evangelist/Developer Advocate. Often intiated by a skip-level manager or Amazonian that found what I did amazing. I put these here as they are less writing focused or less technical.

  • Wiki Metrics Dashboard: Create a dashboard to track teams and roles visiting wiki pages.
  • Enagagement Mechanisms: Gauge interest through a badge/award system for early access contributors.
  • Community Development Initiative: Foster talent with a wiki for economist programs.
  • Team Workshops and Training: Requested by skip-level manager to present a workshop during our Bi-weekly knowledge sharing. I taught SEO best practices, tenets, and how to use VSCode with Git.
  • Broadcast Videos: Budget videos with voice-over, screen capture, and light edits.

Topics

  • ML
  • Infrastructure
  • AWS
  • Spark
  • IDEs
  • Project Management
  • Team Culture
  • Technical Job Role onboarding
  • Various Tech Stacks

Writing Stack

  • Markdown
  • Sphinx
  • HTML/CSS
  • Quip (Docs)

Learnings

Project management

Learned about WIP Limits, and the minor and major differences between each form of Agile Methodology.

Culture

Our team was built around 2 major ideaologies, the first was the general Amazon philsophy of Leader Principles, and the second were organization level tenets.

My Top Leadership Principls: Deliver Results, Earn Trust, Bias for Action.

Technical

Working as a technical writer on AWS Infrastructure taught me a lot about the sheer number of public facing and internal services for cloud computing. I also learned about Spark (Distributed Systems) and state of the art ML research algorithms (confidential).

Testimonials

After learning about the importance of networking and feedback, I developed a feedback form to maintain the connection between my peers and got back some amazing testimonials that speak for themselves.

Scientists

“Nathaniel, you are the first technical writer I met in the company and you did a great job understanding the content of several projects and putting them into nice wiki pages. Thanks for your great work!” - Applied Scientist at Amazon

Engineers

“Nathaniel allowed me to focus on technical development, while being assured that the documentation piece would be well-handled.” - Data Engineer at Amazon

“Nathaniel’s work was always well-polished, and he always was clear about defining requirements.” - Principal Data Engineer at Amazon

“You always bring great energy and are fearless and proactive in learning new things. Your presence on the team will be missed.” - Software Development Engineer at Amazon

Managers

“Nathaniel was excellent to work with. He was organized, his deliverables were bar raising and he was able to complete the project with minimal oversight. He proposed creative ideas to improve the Core AI Intake process, which we adopted.” - Sr. Product Manager at Amazon

“I am impressed on how you streamline our quip to wiki publish process, and all the well polished wiki / guides / posters you wrote.” - Technical Product Management at Amazon

“Nathaniel went above and beyond: really excellent work on the above tasks, and especially helping us define our tenets! - Sr. Research Manager, Core AI (economist and engineering team) at Amazon

Executives

“I’ve seen what you’ve accomplished with the team Wiki pages and would have loved to have the chance to collaborate with you on the Wiki for the whole org.” - Executive Assistant at Amazon

What I will miss

The People. The Resources. The Projects.

The bar of people

Amazon (Core AI) has shown me just how big the world really is. A tech giant that’s able to hire and retain the best through not just an emphasis on monetary compensation, but work-life balance and fostering a suportive, kind, space for learning. Our VPs won a nobel prize and the org has former prize winners. It’s astounding to think about their credentials and qualifications! I truly appreciate their patience when they were explaining (confidential algorithm).

The plentiful resources

Amazon has a strong writing culture, which has led to their content on wikis and broadcast to be truly amazing. I spent countless time learning about work-related AWS Style Guides, Engineer Onboarding, and Amazon All-Hands. On top of that, the sheet number of people led to there being mental-health content for those days with writer’s block. My favorite (non-technical) learnings were on Imposter Syndrome, Choronotypes, and 2 Way Decision Making.

The fascinating projects

I love learning about the ML projects and detailed Data engineering with Spark and Kafka. I’m excited to see where the project leads. Each project felt unique in some way and the org has a good culture about building with legos rather than reinventing the wheel. My favorite part was when I got to document an alpha stage product as it itterated over and grew exponentially in terms of interest.