Cloud data engineer · Portugal · Open to conversations
I turn complex industrial and cloud data into reliable systems—combining Azure, Databricks, Spark and an engineer's instinct for how things fail.
Select a layer to see how I design reliability into the platform.
Capture source meaning, ownership and event time before data begins to move.
Present and past organizations that shaped my path from physical systems to cloud data platforms.
01 / PROFILE
My engineering career started with physical systems.
As a Mechanical Engineer in manufacturing and automotive environments, I learned to think in processes, constraints, reliability, optimization and measurable outcomes. That mindset eventually moved from machines to data.
Today, I apply the same principles to cloud data platforms—designing architectures, pipelines and data products that must remain observable, scalable and maintainable in production.
02 / EXPERIENCE
A career across industrial operations, applied analytics and production data platforms—unified by reliability and measurable outcomes.
Bosch Portugal
Building and operating supply-chain analytics across a SAP-sourced Microsoft data estate, with a focus on data integrity, reliability and decision-critical reporting.
Nordex Group
Delivered legacy-platform migration work toward a modern Azure data stack and built lakehouse pipelines for operational wind-energy data.
Independent
Delivered self-directed data and machine-learning work while completing a postgraduate transition into data engineering.
Yazaki · SIMAN
Designed and industrialized physical systems before moving from machines to data—developing a lasting instinct for constraints, traceability and measurable outcomes.
03 / ENGINEERING PRINCIPLES
The strongest systems make correctness feel ordinary—even when the machinery underneath is not.
Reliability
I design observability and operational ownership with the pipeline—not as a dashboard added after the first incident.
Simplicity
The right architecture is the smallest system that preserves quality, scale and a credible path for change.
Trust
Contracts, lineage, tests and accountable release gates turn technically valid data into information people can use.
Context
A technically elegant pipeline has little value unless it improves a real process, product or business outcome.
04 / SELECTED WORK
No screenshot gallery. Each case study explains the system, the evidence and the boundaries that make its claims credible.
ClinDataGate is an evaluation-first clinical-dataset enhancement POC. It treats every generated value or image as a candidate that must pass explicit fidelity, robustness, privacy and provenance gates—or narrow scope, fall back or abstain.
Migration work across a legacy Microsoft estate and a modern Azure lakehouse—connecting ingestion, Spark transformations, Delta layers, delivery controls and the operational practices needed to keep reporting dependable.
A production Django platform and personal infrastructure lab used to practice secure ingress, reverse-proxy design, containerization, storage and operational automation—without exposing private network details.
An end-to-end renewable-energy business-intelligence pipeline that uses Airflow to make ingestion, transformation and serving dependencies explicit and repeatable.
05 / ARCHITECTURE PLAYGROUND
Inspect a blueprint, then inject a failure and watch its operating controls respond. The market-intelligence view remains a design study—not delivered work.
A layered path from operational sources to trusted data products.
Multiple operational source patterns required explicit schema and ingestion controls.
Click any component to inspect its responsibility.Choose an operating event and replay how contracts, orchestration, quality and release controls contain it.
The incoming schema fingerprint differs from the registered contract.
The affected load is diverted from the trusted processing path.
Consumers remain on the latest validated version.
Mapping, tests and compatibility expectations must be updated.
The release state stays visible until the contract is reconciled.
06 / CAPABILITY MAP
Tools change. The durable capability is knowing how to combine them into a platform that can be trusted and operated.
A visual map of the platforms, languages and operating tools I combine in real data systems.
Designing the movement, transformation and operational ownership of data.
Choosing platform boundaries that match scale, ownership and change.
Building trustworthy layers from source records to analytical products.
Using the right level of abstraction for platform and data-product work.
Making change safe through automation, testing and visible operations.
Applying ML where evaluation proves it adds value—and retaining the baseline when it does not.
07 / ENGINEERING NOTEBOOK
Architecture becomes more useful when the reasoning behind it is visible.
A sophisticated graph that cannot explain a late or incorrect result is weaker than a simpler system with explicit state, ownership and recovery.
08 / FOUNDATIONS
A formal path from physical systems and optimization into modern data platforms, cloud architecture and machine learning.
ISEP · Instituto Superior de Engenharia do Porto
Specialisation: Industrial ManagementISEP · Instituto Superior de Engenharia do Porto
Data Engineering, Big Data & Analytics · Final grade 18/2009 / START A SYSTEM CONVERSATION
Choose an outcome, constraint and priority. The result is a conversation starter—not a generic contact form or an automated estimate.
Brief updated: Modernize a platform, Legacy estate, Reliability first.
Create an incremental path from the current estate to governed, product-oriented data delivery.
Protect existing consumers while responsibilities move in controlled slices.
Begin with the failure model, ownership map and minimum trustworthy release contract.
10 / CONNECT
Choose the channel that fits: inspect the code, connect professionally, or start a direct conversation about a difficult data problem.
GitHub@Tiago-B-C-ReisCode, experiments and production-minded data projects.Explore repositoriesPROFESSIONAL NETWORK↗
LinkedIntiago-b-c-reisExperience, credentials and the easiest way to connect.Connect professionallyDIRECT CHANNEL↗Emailtiago.b.c.reis@hotmail.comThe best route for a focused project, architecture or collaboration conversation.Start a conversation