ENDE

Software, data & digital analytics

Mauricio
Forneron.

Making sense of the systems
behind digital businesses.

I’m a Senior Software Engineer at Flix, with a background in web analytics, tracking, experimentation and data. I enjoy turning messy technical and measurement problems into systems people can trust.

About my work Let’s connect
Connecting the dots
Good systems start with understanding
what they need to make possible.
Currently at FlixA perspective shaped by product, marketing, data and engineering.

01 / About

Technical depth.
A broader perspective.

Having analytics tools installed is one thing. Having data you can trust is another.

Since joining Flix in 2019, I’ve worked across systems, tracking, analytics, experimentation and software in a large digital and e-commerce environment. That experience has taught me to look beyond an individual tool and understand how the pieces fit together.

I work between technical and business teams: clarifying what matters, identifying constraints, and turning ambiguous requirements into a practical solution.

Originally from Argentina, I’ve built my career in Europe and studied Computer Science. I’m curious about complex systems, why they break, and how to make them more useful for the people who depend on them.

02 / Journey

One journey. Many perspectives.

Flix · 2019–present
Current role: Senior Software Engineer

  1. Systems & support

    Starting close to technical operations: understanding how systems work and finding out why they don’t.

  2. Analytics & tracking

    As a Product Analytics & Tracking Specialist, focusing on how user behavior, marketing and digital products are measured.

  3. Experimentation & data

    Connecting implementation with experiment design, data quality and the decisions teams need to make.

  4. Software engineering

    Building and modernizing internal systems, integrations and automation, bringing that measurement perspective into engineering.

03 / Expertise

The work behind the tools.

Different disciplines.
Connected by the same questions.

Tracking & measurement

Designing events, maintaining tracking and debugging unreliable measurement. From marketing integrations to consent-aware collection, the goal is data that reflects what actually happened.

GTM · GA4 · Snowplow · Usercentrics / TCF

Analytics & data

Starting with the business question, defining useful metrics and validating the data behind them. Helping teams interpret behavioral data and understand where the numbers come from.

SQL · Snowflake · Product analytics · Data quality

Experimentation

Working through technical feasibility, experiment design and measurement. Considering statistics, sample size and traffic limitations to understand what an A/B test can—and cannot—prove.

A/B testing · Implementation · Statistical reasoning

Software engineering

Building applications, APIs and integrations; migrating systems and automating repetitive work. Monitoring failures and collaborating with platform and DevOps teams when the work crosses those boundaries.

TypeScript / JavaScript · Node.js · React · Python
Firebase · ConfigCat · Datadog · Exposure to AWS S3 & Bedrock

AI & automation

I’m exploring practical AI applications in software, data and analytics, including Snowflake Cortex, agents and OpenAI APIs. I’m most interested in removing repetitive analytical or technical work, with a clear reason to use AI.

An ongoing exploration

04 / Where I can be useful

Does any of this
sound familiar?

The starting point is usually a problem,
rather than a particular technology.

  • “Our GA4 numbers don’t seem trustworthy.”
  • “We have GTM, but nobody knows what’s firing anymore.”
  • “Marketing, product and analytics report different numbers.”
  • “We collect lots of data, but don’t use much of it.”
  • “We want to run experiments. What can we reliably measure?”
  • “Where could AI actually help our analytics workflow?”

I enjoy untangling these questions—and connecting business requirements with the technical work needed to address them.

05 / My approach

Understand first.
Measure second.
Build what’s needed.

A useful solution starts before the first line of code.

  1. Clarify the question

    What is the actual problem, and what decision needs to be made?

  2. Check the foundations

    What data and systems already exist? Can we trust them, and where are the constraints?

  3. Choose a practical solution

    Then decide what to measure, fix or build. Keep it understandable, maintainable and proportionate to the problem.

06 / Let’s connect

Have a tracking,
analytics or data problem?

I’m always interested in how other teams approach measurement, experimentation and data. Feel free to reach out—we can have a conversation and see whether my experience can help.

Let’s talk Connect on LinkedIn

A question, an idea, or a shared interest.
A conversation is a good place to start.