What the current work consists of.
Ingestion and transformation on Microsoft Fabric: Data Factory pipelines, Lakehouses and Warehouses, SQL, Python and PySpark. Incremental and full-load strategies for large tables, and keeping pipelines dependable when the source systems are not.
Power BI semantic models, dimensional modelling and DAX. Models that follow how the business actually runs, and that stay fast and understandable as they grow.
How an organisation catalogues, owns and documents its reports and datasets, so that people can tell which numbers to trust and whom to ask about them.
Energy, luxury goods, consumer goods and life sciences, in Brazil, Italy and Switzerland.
Business Intelligence Data Engineer
Enterprise BI and data engineering on the Microsoft data platform. I build and maintain the ingestion, transformation and modelling layers behind company reporting, and take part in deciding how the wider analytical environment is organised and governed.
Worldwide Transportation Performance Analyst / Data Scientist
I joined to analyse the performance of the group's worldwide transportation and logistics operations. Over time the role became steadily more technical, moving through data science, data engineering and BI development: from analysing operational performance to designing the data structures used to analyse it.
Trainee / Analyst
Analytical work inside a large multinational: business processes, operational reporting and performance management. This is where I first saw how information moves through an enterprise organisation and how it supports operational decisions.
| Electrolux | InternWorked with data in a controlling environment, an early look at how a multinational measures and reports on its own performance. | 2019Brazil |
| Transluc | Business / Administrative AssistantAutomated parts of day-to-day business processes, the start of an interest in process automation that has stayed with me. | 2018 – 2019Brazil |
| Hi Technologies | InternMy first professional experience, inside a health sciences and laboratory technology startup. | 2016Brazil |
Grouped by how the tools are used, not by self-rating.
| group | items | context |
|---|---|---|
| Day to day |
| What my current work is built on. |
| Architecture and governance |
| The questions around the pipelines and reports. |
| Also worked with |
| From earlier roles, integrations and projects. |
| Outside work |
| Independent projects and current study. |
Certifications and languages.
I didn't start out as a software engineer. My early roles were in administration, business processes and analysis, first in internships and then as a trainee and analyst at ExxonMobil. At Kering I joined as a transportation performance analyst for the group's worldwide logistics, and over time the work moved steadily towards the technical side: from reading the reports, to building them, to designing the models and pipelines behind them. At Hamilton that technical side is now the job.
Having worked on both sides shapes how I approach data problems. A pipeline can run perfectly and still deliver numbers nobody trusts, and a dashboard can look right while describing the business inaccurately. I care about understanding what the data represents, how it is generated and why someone needs it, and then designing the systems underneath so the analysis stays consistent as requirements change.
| AWS Certified Cloud Practitioner | 2023 |
| Google Data Analytics Professional Certificate | 2023 |
| Portuguese | Fluent |
| English | Fluent |
| Italian | Advanced |
| German | Learning |
What I’m studying and thinking about.
Studying how AI agents and language models can be applied to real operational processes: how they interact with enterprise systems, APIs and workflows, and how to judge whether a given use is secure, reliable, affordable and properly supervised by people.
The questions above individual implementations: how an analytical environment should be structured, who owns what, how it is documented, and how people know what to rely on.
Building small applications and experiments outside work, mostly to understand new technologies properly, and investigating how software and automation can improve existing business processes.
Things that don’t belong on a résumé, served through the tools I use at work.
off_hours Mostly outdoors: hiking in the Alps, swimming and cooking Brazilian barbecue. Otherwise chess, rock and trying out new technology for the sake of it.
SELECT category, value
FROM hobbies
ORDER BY category;Press Run to execute the query.
/api/v1/luca/nowSend the request to see what I’m up to.
df = spark.table("favorite_bands")
display(df.orderBy("rank"))display(
df.groupBy("genre")
.count()
.orderBy(F.desc("count"))
)| article | 6,835 chess games, one dashboardHow I turned my chess.com history into an interactive dashboard: a build-time extract, one fact table at game grain, cross-filtering in the browser, and an opening explorer on a real board. | 12 November 2026 |