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Journey to build

Overview

I has a time to build a team from a scratch so I will represented the checkpoint

The milestone checkpoint

  1. Scale of revenue Trading firms can make upwards of $1B in a good year. However, top data firms can make multiples of that. The potential upside for a great financial data firm easily eclipses that of a trading firm in any given year.

You also have to account for the winner-takes-all mentality in trading, where the top two to three firms take home the lion's share of the revenue, leaving the other firms to fall by the wayside.

  1. Equity incentives Trading firms tend to have imbalanced incentives that favor the founder and/or early partners. These individuals capture more of the upside than you'd see in other industries. So much so, that it's rare to see a firm leave a legacy beyond its founder. When you hear about firm spin-offs, it's often because that's the only way a trader can get further career upside.

It's also difficult to put a valuation on company IP and unpredictable revenues, as evident in the low-revenue multiples that stocks like VIRT trade at. The relatively low M&A or private funding activity for trading firms is also an indication of imbalanced incentives and potential upside for non-founders.

  1. Less risk Just like a broker collecting trading commissions or any other trade facilitator, trading firms are directly tied to the volatility and uncertainty of the markets. Data firms, however, are far more defensible from market downturns, changes in volatility, or sudden catastrophic events wherein an entire firm is taken out.

  2. Technology and transferrable skills A trading firm will have a specific tech stack and process, significantly limiting an engineer’s potential to learn and grow—not to mention help build something new. Data firms have a measurable impact on an engineer’s transferable skills, especially when it comes to web stacks.

Web apps have been the centerpiece of the greatest economic successes of the decade. Engineers are more likely to have successful outcomes working at a web-based company than in any other line of work. Yet at a trading firm, even the top firms only have a few web developers (if any) regularly maintaining their public sites.

This discrepancy in emphasis on web development means that engineers at trading firms might miss out on opportunities to enhance their skills in areas like:

Web API design Docker Continuous deployment Zero downtime deployment, In contrast, data firms tend to focus more on these aspects, providing engineers with greater career mobility. Additionally, data firms tackle unique challenges like:

Optimizing for low latency Managing single-threaded versus multiple instances Implementing distributed architectures

For Python Developer

  • Progress standard for create data pipelines and who control

  • Create Finance Glossary with requirement

  • OKR for the team

  • Enhance the team connect with gaming

https://blog.databento.com/blog/best-finance-software-engineering-jobs

  • Apply code style for your team member, this will support the consistent for another to follow

Data Engirneer: datastacktv/data-engineer-roadmap