Data products have become a cornerstone of my work at a startup. This wasn't a sudden shift; it was an organic evolution fueled by my experience in product management. This background provided a strong foundation for understanding data product lifecycles and user needs. The fast-paced, collaborative nature of a startup allows us to wear many hats and leverage the diverse skillsets of our team. This freedom to explore has been instrumental in sharpening my data acumen and fostering a holistic understanding of the data product landscape. In this article, I'll explore how this experience has fostered my growth in the data field.

Don't throw your experience away

Ever feel like life's a giant classroom? That's the mindset of a lifelong learner, someone who constantly seeks valuable lessons from every experience. This growth mindset empowers you to transform your education into a springboard, not a railroad track.

While your degree or past experience might not directly correlate to your dream job, the skills you gained are valuable assets. Even when you shift careers, the critical thinking, problem-solving, and communication skills you honed in yourprevious field can be powerfully repurposed for your new path.

End-to-end Product Management

The experiences I have accumulated in the product area have given me a holistic view of the technical and non-technical needs involved from the Discovery process and idealization of solutions to the operational, the act of implementing such solutions and observing the target audience's adherence.

This comprehensive perception enabled me to understand the demands of designers, developers, and any other role responsible for idealizing the solution or new functionality for the product. It was also very useful to predict possible bottlenecks in the processes that could delay the roadmap of tasks that every self-respecting startup has designed.

Another cool point was more focused on metrics definition and product analytics. Understanding which metrics make sense for each area, understanding which KPIs make sense for the product, etc. This was and is being developed a lot due to my work in the data area, but certainly the understanding of some important metrics came from the product background.

Learn how the process works

Often, in the product development process, important solutions and decisive features are idealized by people with a generalist and creative bias (and don't get me wrong, these participations make all the difference in shaping the solution), but these profiles usually don't understand the feasibility factor at the code level (often backend) or the cost of a new functionality, as it can happen that the feature is feasible, but very expensive and not worth it at that time.

When I made my career change, I noticed how much it makes a difference to know a little more about the details of some solutions. Certainly a Product Manager with a technical bias has a lot of advantage in this aspect, but in the technical area itself, being a hybrid helps a lot. I will make a specific post about this and about my role as Analytics Engineer.

If you already have skills such as organization, prioritization, and self-management, working with multidisciplinary teams will boost these characteristics and make them even more evident. Working with technical teams, this makes all the difference, as often developers, engineers, and data scientists need a "translation" step, where people involved with the product and have greater knowledge of the whole pass the baton with more specific details of the business rules, so that the development of the solution can happen.

Making Strategies Tangible

Here's where product skills bridge the gap between the company's strategic vision (reaching the market, growth) and the actionable roadmap.

This can be a tightrope walk. The biggest hurdle? Balancing confidence in your team's delivery estimates with a realistic understanding of technical feasibility. Even basic technical fluency empowers you to craft compelling arguments that translate strategy into actionable tasks.

You can also readily propose improvements, adjustments, and course corrections that directly impact the return on investment. Recognizing this knowledge gap, I'm actively expanding my technical skillset.

My current focus is on structuring complex SQL queries, including the use of CTEs. Additionally, I'm delving deeper into Python development (beyond the web scraping and data cleaning I explored last year) to leverage its power and versatility in data analysis.

So, I certainly have a lot to learn, but I also have a lot of respect for everything I have learned so far. If you are also making a career change to the data area, or have any questions regarding this article, you can reach me on LinkedIn!

What will I learn tomorrow? Where will this all take me? Stay tuned and find out. ツ