Hello. I'm Alejandro Amo.

Who am I?

I would describe myself as an IT systems adminitrator who has been slowly adding software development and project management to the skill set to cover the ever growing needs of my employers.

Some key concepts to understand my profile and posture:

• I'm into all things data: data migration using tailored solutions, development of APIs for data integration between systems, design of data models for new projects, development of dashboards for data exploitation...
• Python is my swiss army knife of choice. While I can do many things with good old Bash💟, I tend to develop and document custom solutions for the needs of organizations using Python and its versatile library of modules as a foundation.
• The cloud infrastructure I'm more used to is Amazon's, but I don't fear Microsoft's or Google's. I prefer cloud agnostic stacks, though!
• I also have some basic experience in mobile app developemnt. I have developed some Web Progressive Apps on Angular, and I find particularly enjoyable developing the frontend for my own backends from time to time 😉.

Besides developing and integrating common enterprise apps for managing all sorts of data, other topics I have covered in my career are geolocation data, cybersecurity (smart cars, electricity and water utilities, banking) and edutech (Student Information Systems, Learning Management Systems, data integration standards like LTI).

What stuff do I love to work in?

When it comes to managing enterprise data, two important things are worth considering, the data quality and FAIR principles.

Data Quality

Data quality refers to the fitness of data for its intended use. It encompasses various aspects such as accuracy, completeness, consistency, timeliness, and relevance.

If you run a business or perform a research project, you probably realized that the quality of its data can decay over time for a variety of reasons. Data is often entered manually, people can make mistakes, two sources of information are not exactly the same but are mixed together, illegal values appear, traceability becomes impossible... over time, these mistakes can accumulate, leading to inaccurate or inconsistent data. In addition, data can become outdated as business processes change, new technologies are introduced, or regulatory requirements evolve, complicating the things even further.

Without proper care of data quality, organizations risk making decisions based on inaccurate or incomplete data, which can have serious consequences. For example, a business may make a poor strategic decision based on faulty data, or a researcher may draw incorrect conclusions from data that has not been properly validated.

The ultimate goal in such cases is clear: developing a strategy and deploying new technologies and processes that help make your data sources become accurate, consistent, and relevant again. By doing so, you can make more informed decisions and gain a competitive advantage in your field.

FAIR principles

In data engineering, four typical needs of data have become popular as a single concept known as the FAIR data principles (Findable, Accessible, Interoperable and Reusable).

FAIR data principles are a set of guiding principles for achieving such goals. They provide a framework for managing and sharing data in a way that maximizes its potential for reuse and discovery. In order to achieve that, we usually focus in making the data fully machine-readable, provinding useful metadata, attaching persistent and unique identifiers, and apply standard vocabularies. In this way, the data can be easily discovered, accessed, and used by humans and machines alike. Projects that aim for attaining FAIR principles can tap into a lot of added value business intelligence wise.

How to contact me

Do you want to get in touch? Do you have a project proposal for me? Here are the best ways to get in touch with me:

(don't hesitate to take a look at some of my repositories or ask more info about any specific project that may draw your attention).