Over the past 15 years, I've worked with data across a range of domains: - **Poker** – Learned to spot patterns and think probabilistically using online game data. - [**Amazon**](https://amazon.com) – Worked with large-scale e-commerce and recommendation system data. - [**PitchBook**](https://pitchbook.com) – Focused on private company data like employee counts, revenue, and funding. - **[Norwest Venture Partners](https://nvp.com)**– Built models to understand investor to company interactions and signals of success. These experiences have shaped how I approach data work today — from raw ingestion to polished insight that takes shape as self-service, usable products. --------- Whenever I begin a new data initiative — whether it's sourcing company data, building ML models, or creating dashboards — I start with a simple question: > **Do I (or my team) have the right tools across the stack to get this done right — and done fast?** These tools span multiple layers of the data lifecycle. Below is a mental model I keep in mind, organized by category and linked to the tools I most commonly reach for. ## Tooling Areas - [[Code Repository|Code Repository]] - [[Cloud Infrastructure|Cloud Infrastructure]] - [[IDE (Integrated Development Environment)|IDE]] - [[Data Engineering Tools|Data Engineering Tools]] - [[Data Science & Analytics]] - [[Visualization and Reporting|Visualization and Reporting]] - [[AI Tools]] ---- #### Sample Workflow With these building blocks, I can confidently take a project from raw input to insight: 1. **Extract** data from sources (web, APIs, vendors) 2. **Load** into Postgres or a data lake (AWS S3) 3. **Transform** it using dbt or Python 4. **Automate steps 1–3** with Dagster or similar orchestration 5. **Score or Analyze** via Scikit-learn, Hugging Face, etc. 6. **Visualize** insights via Tableau, Metabase or Sigma ------- ## Final Thoughts After mapping out the right tools across the stack, one truth keeps coming up: > “Most failed data projects don’t fail from bad modeling — they fail because infrastructure was never stabilized, or the right tools weren’t in place.” But that’s only part of it. Even with the right tools, projects fall apart if the **foundations of good data work** aren’t there: - **Data quality** must be verified and maintained — pipelines are only as valuable as the trust you can place in them. - **Naming conventions and consistency** might seem minor in the moment, but they’re how your future self — or your team — makes sense of what you built. The instant someone takes a peek at your schema, they should understand what it is they're looking at... not have to guess. - And just as important: we as data practitioners have a responsibility to **bridge the gap between the data and the business** — to not just build, but explain. Help others understand why it matters, where the caveats are, and how to make decisions confidently with data. A complete stack is just the beginning. Thoughtful process, clear structure, and strong communication are what turn tools into impact. ----- ## About Me I'm a leader and data professional with experience across private markets, e-commerce, and applied machine learning. I use this vault to organize my thoughts, experiment with ideas, and document what I've learned — both for myself and anyone who might find it useful. I also use it broadly enough to demonstrate my knowledge of the data realm and what goes into building good data products. Feel free to reach out if something here resonates or you'd like to collaborate. I also do public speaking for small to medium sized groups, if thats something you're interested in as well please reach out. Mentions: [Data Collection at PitchBook](https://pitchbook.com/blog/from-the-lab-scaling-pitchbooks-data-collection-with-ner) [Building a Data Team at PitchBook]([https://pitchbook.com/blog/product-qa-with-tyler-martinez-growing-a-technology-team-in-seattle](https://pitchbook.com/blog/product-qa-with-tyler-martinez-growing-a-technology-team-in-seattle)) 📧 Email: [[email protected]](mailto:[email protected]) 🔗 LinkedIn: [https://www.linkedin.com/in/tyler-martinez-759b4636](https://www.linkedin.com/in/tyler-martinez-759b4636) 🌐 Website: [oriems.com](https://oriems.com)