Javi on DataData • AI • Cloud Engineering

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Hi, I'm Javi.

I'm a data engineering leader focused on designing, building and operating large-scale data platforms across cloud environments.

My work sits at the intersection of data architecture, performance, engineering practices and production operations.

I spend a lot of time working with technologies such as SQL Server, Microsoft Fabric, Azure, Python, Delta Lake and modern cloud data platforms.

But the technology itself is only part of the job.

What interests me most is how systems behave when they become real: larger datasets, more users, more integrations, more deployments and more things that can fail.

What I work on

  • Data Engineering

    Designing ingestion, transformation and serving architectures.

  • Cloud Architecture

    Building scalable data systems across modern cloud environments.

  • Microsoft Data Platform

    SQL Server, Microsoft Fabric, Azure and analytics platforms.

  • Performance & Scale

    Systems dealing with hundreds of millions or billions of rows.

  • AI & Data

    Practical use of LLMs, APIs and automation around enterprise data.

  • Engineering Practices

    Git, CI/CD, observability, debugging, testing and production operations.

How I think about engineering

I prefer practical engineering over architecture for architecture's sake.

  1. Start with the problem

    Technology choices should follow the workload, not trends.

  2. Measure before changing

    Performance work should begin with evidence.

  3. Design for operations

    Logging, deployment, debugging and recovery are part of the architecture.

  4. Keep complexity intentional

    More layers, services and abstractions are not automatically better.

  5. Build for change

    Data platforms, technologies and business requirements evolve.

  6. Share what you learn

    Explaining a problem often makes the solution better.

Why Javi on Data

I created Javi on Data to document the engineering lessons that are easy to lose when projects move on.

The goal is to turn practical experience into useful material for other engineers:

Some content starts with a simple question. Other pieces come from production problems, performance investigations, architecture tradeoffs or experiments.

The objective is the same: make complex data engineering problems easier to understand and apply.

Learn, build, share

Javi on Data is built around a simple idea: learning becomes more valuable when it is turned into something other people can use.

  1. LearnStudy a problem in depth
  2. BuildTurn it into working code
  3. PublishWrite it down clearly
  4. SharePresent it to others
  5. DiscussCompare notes with engineers
  6. ImproveFeed lessons back in
The cycle behind the site: each stage feeds the next, and discussion leads back to better work.

Where each stage lives on the site

What I'm exploring now

Areas of technical focus, with what this site already covers on each. They are interests and ongoing work, not a list of achievements.

Who this site is for

The same content serves two kinds of readers, from different angles.

Students & engineers learning

  • Understand concepts
  • Follow practical tutorials
  • Explore architecture
  • Build projects
  • Learn production thinking early

Experienced professionals

  • Compare architecture patterns
  • Study production tradeoffs
  • Improve performance
  • Reuse checklists and references
  • Discuss real engineering problems

Let's connect

Have a question, want to discuss a data engineering problem, collaborate on a project, or invite me to speak?

Send me a message Connect on LinkedIn