Darlan Noetzold, MSc

Developer & Researcher

noetzold.tech

Developer & Researcher

PROFILE

Ph.D Student at University of Salamanca, Spain. Master in Applied Computing from UNISINOS and Computer Scientist graduated from IFSul, currently working as a Mid-level Programmer at CWI Software. I also hold an MBA in Big Data and Competitive Intelligence, as well as a postgraduate degree in Software Engineering and Cloud Computing. I have always been passionate about Mathematics and creating. I feel that programming and the technology field in general can associate both worlds. This convergence of logic and creativity has always inspired me to think further and execute my projects. Combined with a study routine and dedication to what I love, it has brought me to where I am today. Without a doubt, this area is my source of dopamine at the end of the day.

TECHNICAL SKILLS

Backend & APIs

JAVA
JAVA EE
Spring Framework
Quarkus
Python (FastAPI, Django, Flask)
Microservices (API’s, Mocks)
Node.js/Nest.js
Message Brokers (Kafka, RabbitMq, ActiveMq)
Caching (Redis)
E-commerce

Frontend & Mobile

React
HTML5 & CSS3
JavaScript/TypeScript
Android (Native, Flutter)

Cloud & Infrastructure

DevOps (Docker, Vagrant, K8s)
Clouds (AWS, OCI, Google Cloud)
OS (Linux, Windows)
Raspberry Pi
Network Infrastructure

Quality, Architecture & Security

Java Profiling (JMR, JMeter, VisualVM, JProfile)
Tests (Junit, Mockito, JMeter)
CyberSecurity (Data Privacy, Cryptography)
Observability (Graylog, Dynatrace, Grafana)
Project/Design Patterns
Rust

FOCUS OF STUDY

Java

Knowledge of Object-Oriented Java, Exceptions, Java.lang Package, Java.io Package, Java.util Package, Collections, Maven, XML, Servlets, JPA, Spring Framework, JUnit, JSF, Hibernate, RESTful APIs, Microservices, Docker, Kubernetes, Jenkins, Kafka, Redis, Elasticsearch, Git, GitLab CI/CD, Gradle, Lombok, and Swagger. Hands-on development with the aforementioned technologies, including problem-solving in real and current applications in the market, and experience in continuous integration, automated deployment in production environments, and distributed and scalable systems architecture.

Machine Learning

Data Science complement with Python and its libraries (Numpy, Pandas, Seaborn, Matplotlib and Scikit-learn), as well as tools such as MathLab and mathematical concepts applied in Data Science. Experience in advanced techniques such as Neural Networks, Learning Coefficient, Normal Equation, Normalization, Classification, Gradient Descent, Principal Component Analysis (PCA), L1/L2 Regulation, and optimization methods such as Adam and RMSprop. Knowledge in the practical application of these techniques to solve complex problems in data analysis and machine learning.

Python

Knowledge of Python 2.7 and 3 Object Oriented such as the use of String Manipulation, Collections, Brazilian standards and Test Classes, creation of APIs with Flask and Django. In addition to practice with Libraries such as TKinter, PyGame, Sklearn, Pandas, Numpy, Matplotlib and PyTorch.

EXPERIENCE

Senior Programmer at CWI –

August 2025 – the moment

Mid-level Programmer at CWI –

December 2023 – August 2025

Junior Programmer at Compass.UOL –

April 2022 – November 2023

Trainee Programmer at Compass.UOL –

May 2021 – March 2022

IFSul project with EMBRAPA extension –

August 2020 – May 2021

Independent Projects

Projects on GitHub

Published Scientific Articles

Projects Page

My GitHub

ORCID

01 / RESEARCH

Adaptive Security Engineering

Research on security mechanisms that adapt cryptographic choices to changing contexts, balancing protection, performance, and operational requirements. This direction is informed by work on PRISEC III and the Q-OPSEC adaptive middleware proposal.

Context-aware security · Adaptive cryptography · Risk-informed protection

02 / RESEARCH

Self-Adaptive Smart Systems

Design of intelligent environments that observe their operating context, anticipate changing conditions, and adjust system behavior to sustain performance and service quality. Inspired by the Oraculum model for self-adaptive optimization in smart environments.

Self-adaptation · Predictive optimization · Smart environments

03 / PRACTICE

Software Architecture

Building maintainable, evolvable software through clear boundaries, explicit domain models, and deliberate dependency direction. Applied concepts include Domain-Driven Design, proven design patterns, Clean Architecture, Hexagonal Architecture, and Onion Architecture.

DDD · Design patterns · Clean / Hexagonal / Onion Architecture