With this PC, I took my first steps building sites with Adobe Flash and PHP, a combo long gone, but one that set me on the developer path I plan to walk for as long as I can.
This is where this path has brought me so far. For more details, check the 🔬 Project Case Studies.
| Sector | Company | Stack / Role | Duration |
|---|---|---|---|
| Startup | Neurogram | React Web | Nov 2023 - Nov 2024 |
| Startup | XTeam | React Native | Sep 2022 - Jul 2023 |
| Retail | Riachuelo | React Native | Aug 2020 - Aug 2022 |
| Banking | Safra | AngularJS | Jun 2019 - Aug 2020 |
| Banking | Itaú | Angular 2+ | Mar 2018 - Jun 2019 |
| Academia | UEPG | Java | Mar 2015 - Aug 2017 |
| Insurance | Virtual | Delphi / MEAN | May 2013 - Mar 2015 |
| Self-Employed | Freelance | Software and hardware technician | 2004 - 2008 |
I’m seeking a role in an environment that embraces
transparency and communication, where clear
processes and collaboration can bring out
the best in my professional values and
skills.
My goal is to join or help a team become
high-performing, deliver an unforgettable
developer and user experience, and improve the
quality of life for both the team and the
end users.
Through my previous roles, I’ve learned values that go beyond the code, values that support the team from concept to release. These are the values I bring to every team I join.
This is my top priority. I make sure the team knows when a task will be done, how we plan to develop it, and why we approach it that way.
If we can’t deliver on time, the next step is to
discuss openly what can be done within the time we
have.
Delaying bad news, keeping a task “almost
done” for weeks, or omitting the release date
are just ways of avoiding accountability. That is why
we commit to transparency, because when we
share issues early, we gain room to react and
adapt together.
Every development team has a process, even if it is
‘Go Horse’, it is still a process.
We seek to understand how the process
works, document it clearly, and
improve it step by step.
This is the core of Agile practice: building
predictability and achieving sustainable
delivery over time.
Frontend codebases “die” on average
in 5 years through rewrites, framework shifts, or
redesigns.
Backend codebases last on average 10
years before major replacement or replatforming.
But a lost user is lost forever.
New features, analytics, redesigns, and refactorings
mean nothing if the user is
gone.
That is why we prioritize user impact above all, making
sure every decision serves what is best for the
final user.
I treat the software as if I am the
owner.
That means caring about quality, stability, and
user experience, not just moving
tasks to “done.”
After years of dealing with bad code, I feel responsible for long-term maintainability and for always leaving the codebase better than I found it.
If you don’t take time to maintain the code, the code will take the time for you.
Maintenance is always required.
To save maintenance costs, I write
clean, structured code from the
start.
When that is not possible, I
refactor bad patterns, improve readability, and
simplify structures.
Sometimes a codebase needs significant maintenance, and
the only safe way is for the team to
acknowledge it and schedule time for it.
Healthy code is not always
resilient.
A codebase can be clean and organized, yet still
collapse at runtime under unexpected cases or
heavy load.
A mindful developer considers the code, the hardware, the environment, the data flow, and system behavior under pressure.
That is why I code for the worst case: catching exceptions, validating input data, creating fallbacks instead of assuming the happy path, and logging external processes.
My interest in high quality code goes beyond work and deep into my personal interests. But I also have others, here is a list:
My career objective has been shaped by every project I’ve worked
on.
Here is how I contributed to each of them, presented using the
STAR (Situation, Task, Action, Result) approach.
The previous codebase combined Rails, React, Tailwind, GraphQL, and Docker across multiple repositories with duplicated components and scattered configs. This fragmentation caused long onboarding times, inconsistent standards, and clear signs of vendor lock-in.
Considering the in-house team’s low seniority and the complexity of the project, I set out to reshape the project’s stack, replacing dependency hell with an architecture that was simple, maintainable, and sustainable, pursuing the following objectives:
My first step was to assess what could be salvaged from the frontend. After several attempts, I confirmed that refactoring would be slower than starting fresh. Between Next.js and Vite, the latter was chosen for its faster builds, simpler configuration, and better alignment with the backend stack based on Firebase and Google Cloud Platform.
Instead of leaving developers to wrestle with separate Babel, PostCSS, Tailwind, and Docker setups, I collapsed dependencies into one consistent Vite configuration. This dramatically improved setup time and enabled features like obfuscation and vendor chunking by default.
Below is the build configuration file I introduced:
import react from "@vitejs/plugin-react";
import tsconfigPaths from "vite-tsconfig-paths";
import obfuscatorPlugin from "vite-plugin-javascript-obfuscator";
type buildConfigOptions = {
manualChunks?: boolean;
vendors?: string[];
};
export const buildConfig =
({ vendors, manualChunks }: buildConfigOptions = {}) =>
({ mode, command }) => {
const isProdBuild = ...;
const noMinify = ...;
const minify = ...;
const vendorPath = [
"jsencrypt",
"i18n",
"yup",
"lodash",
"dayjs",
...
"lib-framework/src/fonts",
"lib-framework/src/icons",
"lib-framework/src/assets",
"lib-framework/src/hooks",
"lib-framework/src/tokens",
"lib-framework/src/components",
...(vendors || []),
];
const config = {
define: {
"process.env": {},
},
plugins: [
react(),
tsconfigPaths(),
obfuscatorPlugin({
apply: () => isProdBuild,
...
}),
],
build: {
minify,
rollupOptions: {
treeshake: true,
output: {
manualChunks(id: any) {
if (!manualChunks) {
...
}
for (const vendor of vendorPath) {
...;
}
return ...;
},
},
},
},
...
return config;
};The deployment pipeline was entirely controlled by the consultancy, including the production and staging environments. Deployments were triggered automatically with every change, but the maturity of the software and the team was not ready for such automation. As a result, bugs were introduced directly into production, breaking the user experience and creating unnecessary troubleshooting overhead for the team.
Shift the ownership of the pipeline and environments back to the company, simplify the deployment, and give full control to the in-house team. The objective was to design a fast, easy, and manual deployment flow that would only run when a developer explicitly triggered it, reducing accidental breakages in production.
I redesigned the deployment process using the minimum resources and complexity possible. Since the company was part of the Google for Startups program, the infrastructure of choice was Firebase. To make deployments simple and predictable, I created a GitHub Actions workflow that consolidated all frontend projects one codebase. This ensured the process was manual, quick, and transparent, while eliminating the hidden consultancy-owned pipelines.
Below is the GitHub Action I authored to handle all frontend projects in one place:
name: manual deploy
on:
workflow_dispatch:
inputs:
project-name:
type: choice
...
build-type:
type: choice
...
env:
...
run-name: ${{ inputs.build-type }} TO ${{ inputs.project-name }} AT ${{ github.event.repository.pushed_at }} WITH ${{ github.sha }}
jobs:
deploy_firebase:
runs-on: ubuntu-latest
defaults:
run:
working-directory: ${{ github.workspace }}/proj-${{inputs.project-name}}/
steps:
...
- name: Deploy
run: |
curl -sL https://firebase.tools | bash
firebase deploy --only hosting:target-${{inputs.project-name}} --token ... --project=project-${{inputs.project-name}}-DEV --config="../lib-framework/firebase.json"Multiple projects implemented the same logic for API calls, encryption, i18n, and UI components. This led to frequent code duplication, inconsistencies between projects, and bugs caused by drift in how core features were handled.
Eliminate duplicated logic by creating a single framework that standardized core features and could be reused across all projects. The goal was to ensure consistency, reduce maintenance overhead, and accelerate new project setups.
I authored a centralized internal library-framework that
included:
1. A custom axios layer with interceptors and typed
adapters.
2. A unified crypto module using a hybrid combination
of RSA and AES.
3. Mock Service Worker patterns and mock data for
consistent testing and development.
4. Preconfigured project templates and setup files to
enable fast project creation. 5. Providers for Firebase,
context, i18n, overlay, and query handling.
6. Reusable UI components and design tokens.
Before my involvement, the app was built only with a dozen hard-coded entries. This limited dataset masked scalability issues in the fetching and rendering logic. After I integrated the backend, the app received thousands of real bottle registries, and the existing implementation could not handle this scale, causing the app to freeze during feching, filtering, grouping, and searching.
Re-architect how the app handled large-scale data
to:
1. Build a modular and maintainable architecture for
data and UI.
2. Enhance user experience with smooth navigation and
search.
3. Ensure accurate results across all features.
4. Support thousands of records without freezing.
First I needed to guarantee the data was loaded quickly and accurately. For that I reviewed how the app was fetching and storing information and found deeply nested loops, duplicated logic and recalculations on every action. To reorganize the data fetching and storage flow, I created consistent patterns for how records were requested, saved and displayed. I also introduced a preloading logic that ensured data was available before the UI rendered. This eliminated unnecessary reload cycles and gave users a faster and smoother experience from cold boot.
After that I addressed the UI data rendering code. Many filtering and sorting elements were duplicated and inconsistent so I refactored them into reusable components which made the interface easier to maintain and extend. To improve the experience of browsing large inventories I introduced sectioned lists and infinite scroll which reduced rendering cost and gave users a smooth and responsive navigation.
Below is one of the optimizations I introduced to the selectors.
- export const selectCustomFilters = (...) => {
- ...
- return {
- locations: [
- ...new Set(
- cellar
- .map(i => {
- if (i.Holdings) {
- return i.Holdings.map(holding => {
- if (holding.Locations) {
- return holding?.Locations.map(k => {
- return k.Location;
- });
- } else {
- return;
- }
- }).flat();
- }
- return;
- })
- .flat()
- .filter(i => typeof i === 'string'),
- ),
- ],
- ...
+ const FilterPendingDataArray = (inCellarWines: InCellarWinePending[]) => {
+ const resultData = {} as FilterObject<PendingFilters>;
+
+ inCellarWines?.forEach?.(wine => {
+ const hasBottles = wine?.Purchases?.some?.(p => !!p?.Quantity);
+ if (!hasBottles) return;
+
+ addNewFilterDataItem(resultData, 'appellation', wine?.Appellation);
+ ...
+
+ wine?.Purchases?.forEach(p => {
+ if (!p?.Quantity) return;
+ addNewFilterDataItem(resultData, 'bottleSize', p?.Size);
+ });
+ });
+
+ const { appellation, bottleSize, country, masterVarietal, region, subRegion, type } = resultData;
+ [appellation, bottleSize, country, masterVarietal, region, subRegion, type].forEach(a => a?.sort?.(sortStringAsc));
+
+ const { vintage } = resultData;
+ [vintage].forEach(a => a?.sort?.(sortNumberAsc));
+
+ return resultData;
+ };After we managed to consistently recover thousands of entries across the app, the next challenge was the lack of a proper model to organize, sort, and feed data into the UI. The core issue was the same as before: the data model was partially incorrect and hardcoded. In addition, the backend did not provide a reliable way to validate its payloads, and in many cases critical fields were missing.
Create a reliable way to handle the app’s deeply
nested and inconsistent backend data in order to:
1. Enable users to quickly find and browse bottles with
accurate results.
2. Ensure the model could scale to thousands of entries
without breaking.
3. Establish a consistent foundation for filtering and
search.
4. Resolve issues caused by missing and
unreliable fields.
To solve this I restructured the data handling into a graph-structured traversal model where each level of information (bottles, holdings, locations, bins) was treated as a connected node. This approach created a navigable structure where starting from a node like a location you could immediately find the bottles stored there and from those bottles trace back to their vintage or other attributes. This replaced scattered nested loops with a clear and predictable flow, making the model easier to maintain, extend, and scale.
This graph approach gave three major advantages:
1. Consistency: the same traversal logic powered
cellar, pending, and consumed states, removing duplication and
errors.
2. Extensibility: adding a new filter meant only
extending traversal rules for a node, not rewriting entire loops.
3. Traversal clarity: instead of nested loops, each
level of the data (wine → holding → location → bin) contributed in an
organized way.
The app serves a global audience of wine collectors who expect multiple language support. While there were some early attempts at internationalization, the application was inconsistent and incomplete. Many components still relied on hardcoded English strings for filters, chips, dropdowns, and error messages. This incomplete approach made the UI feel disjointed and awkward, blocking full localization and limiting the app’s ability to deliver a scalable, accessible international experience.
Establish a consistent internationalization
pattern to:
1. Create a scalable i18n foundation that developers
could apply uniformly across the app.
2. Ensure UI elements like filters, chips, and dialogs were
fully translation-ready.
3. Fix prior inconsistencies and enable a seamless multilingual
experience for end users.
4. Replace remaining hardcoded strings
with localized messages.
I refactored the application to use a consistent internationalization pattern, replacing static strings with translated messages across components. To simplify adoption, I created hooks to make it easy to pull translations into any new component.
Below is the hook I introduced, which encapsulated the logic for pulling translation messages, formatting them with react-intl, and wiring them into navigation flows. This removed duplication and made internationalization extensible across filters, chips, bottom sheets, and dropdowns:
import {useNavigation, useRoute} from '@react-navigation/native';
import {useIntl} from 'react-intl';
import {useEffect, useRef} from 'react';
const useEventActionSheet = ({messages, title, params, onSelect}) => {
const route = useRoute();
const {formatMessage} = useIntl();
const navigation = useNavigation();
...
useEffect(() => {...}, [route?.params]);
const openSheet = () => {
const options = Object.keys(messages).map(key => {
const message = messages[key];
return {
label: formatMessage(message),
value: message.value,
};
});
navigation.navigate('EventActionSheet', {...});
};
return [openSheet];
};
export default useEventActionSheet;
The company launched a security initiative requiring protection of confidential data on rooted devices, emulators, and man in the middle attacks. It required to strengthen security beyond HTTPS and ensure compliance without adding latency or disrupting the user experience.
Implement an additional security mechanism to protect data exchanged between React Native and Java endpoints. Ensure key management, payload encryption, and compatibility with existing systems.
Instead of relying solely on HTTPS, I implemented an RSA + AES model, where each request was encrypted and decrypted on both React Native client and Java Server.
On the frontend, I created a TypeScript module that generated AES keys per session, encrypted them with the public RSA key, and transparently handled encryption and decryption through Axios interceptors.
On the backend, I developed a Java utility library to mirror this logic. It managed key exchange, payload decryption, and response re-encryption, ensuring perfect alignment between platforms.
Below is Axios interceptor to encrypt payloads, creating a transparent and reusable security layer across the entire app.
import { AxiosRequestConfig, AxiosResponse } from 'axios';
import { ... } from '~/utils/crypto';
import { ... } from '~/utils/env';
import { Interceptor } from './types';
const onRequest = async (
config: Promise<AxiosRequestConfig>,
): Promise<AxiosRequestConfig> => {
const oldConfig = await config;
const { data: value } = oldConfig;
const publicKey = await ...();
const newConfig = {
...oldConfig,
data: {
data: encrypt({
publicKey,
value,
}),
},
};
return newConfig;
};
export const hybridEncryptInt: Interceptor = {
onRequest,
};
const onResponse = async (
response: Promise<AxiosResponse>,
): Promise<AxiosResponse> => {
const _response = await response;
try {
const privateKey = await ...();
const data = decrypt({
privateKey,
value: _response.data.data,
});
return { ..._response, data };
} catch (err) {
console.error(`[HYBRID DECRYPT ERROR]`, err);
return _response;
}
};
export const hybridDecryptInt: Interceptor = {
onResponse,
};Below is the Java counterpart that handled the server-side decryption and encryption.
package hybridCrypto;
public class RSACrypt implements Serializable {
public static void generateKeys() {
try {
KeyPairGenerator keyGen = KeyPairGenerator.getInstance("RSA");
...
} catch (Exception e) {
...
e.printStackTrace();
}
}
private static PublicKey getPublicKey(String base64PublicKey) throws Exception {
try {
X509EncodedKeySpec keySpec = new ...;
return KeyFactory.getInstance("RSA").generatePublic(keySpec);
} catch (Exception e) {
throw new Exception("Erro na chave pública", e);
}
}
private static PrivateKey getPrivateKey(String base64PrivateKey) throws Exception {
try {
PKCS8EncodedKeySpec keySpec = new ...;
return KeyFactory.getInstance("RSA").generatePrivate(keySpec);
} catch (Exception e) {
throw new Exception("Erro na chave privada", e);
}
}
private static byte[] encrypt(String plainText, PublicKey publicKey) throws Exception {
Cipher cipher = getCipher();
cipher.init(...);
return cipher.doFinal(...);
}
private static byte[] decrypt(byte[] cipherText, PrivateKey privateKey) throws Exception {
Cipher cipher = getCipher();
cipher.init(...);
return cipher.doFinal(...);
}
private static Cipher getCipher() throws Exception {
return Cipher.getInstance(...);
}
public static String aesEncrypt(String plainText, String base64PublicKey) throws Exception {
PublicKey publicKey = ...;
byte[] cipherText = ...;
return toBase64(cipherText);
}
public static String hybridEncrypt(String plainText, String base64PublicKey) throws Exception {
AESCrypt aescrypt = new AESCrypt();
String aesEncryptedData = aescrypt.encrypt(...);
String encryptedText = ...;
return encryptedText;
}
public static String decrypt(String cipherText, String base64PrivateKey) throws Exception {
byte[] cipherBytes;
cipherBytes = fromBase64(cipherText);
try {
PrivateKey privateKey = ...;
String decryptedText = ...;
return decryptedText;
} catch (Exception e) {
throw new Exception("Erro na decriptacao", e);
}
}
public static String hybridDecrypt(String encryptedText, String base64PrivateKey) throws Exception {
AESCrypt aescrypt = new AESCrypt();
String decriptedData = ...;
return decriptedData;
}
private static byte[] fromBase64(String str) {
return DatatypeConverter.parseBase64Binary(str);
}
private static String toBase64(byte[] ba) {
return DatatypeConverter.printBase64Binary(ba);
}
}Initially built with React Native Paper as the design choice, the project later underwent UI redesigns and custom management requests that pushed the library beyond its intended scope. The team had stretched component customizations to their limit, creating complex overrides and inconsistent layouts. This led to design drift across the app and made the codebase increasingly difficult to maintain and scale.
Replace the overextended UI library with a flexible styling system that could keep pace with frequent design changes. The new solution needed to let developers build and style components in one place, reduce the need for overrides, and speed up page creation without sacrificing consistency or readability.
I designed and built React Native String Style, an inline styling tool inspired by Tailwind CSS, to replace React Native Paper and eliminate dependency on rigid components. It enabled developers to write utility-based class strings directly in JSX, merging structure and styling in a single file.
I refactored screens and components to adopt this syntax, simplifying layout creation and removing the need for complex styled component files. I also introduced design tokens for colors, spacing, and typography, enabling quick global updates whenever the design system changed.
Below is an example showing the two ways to use the styling tool: converting objects to styles with objToRNStyle() and applying inline utility classes with sstyle.
export const RadioButton: React.FC<RadioButtonProps> = (...}) => {
const [selectedItem, setSelectedItem] = useState(value);
const handleOnPress = (radioItem: RadioItem) => {
setSelectedItem(radioItem);
if (onPress) onPress(radioItem);
};
const buttonStyle = objToRNStyle({
position: 'jcc aic fg',
height: 'min-height-36',
border: 'w-100% bd-ra-4 bg-radioButton.bg bd-width-1 bd-style-solid',
active: 'bd-color-radioButton.bd.active',
inactive: 'bd-color-radioButton.bd.inactive',
});
return (
<>
{!!title && (
<View sstyle={`pd-b-16${hp ? ' pd-l-16' : ''}`}>
<Text sstyle="fs-13 lh-16 ff-me c-text.title">{title || ''}</Text>
</View>
)}
<View sstyle={`fdr${hp ? ' pd-h-' + hp : ''}`}>
{radioItems.map((radioItem, index) => {
const active = selectedItem?.value === radioItem.value;
const lastItem = index === (radioItems?.length || 1) - 1;
return (
<View sstyle={lastItem ? 'fg' : 'fg pd-r-8'} key={index}>
<TouchableOpacity onPress={() => handleOnPress(radioItem)}>
<View
style={[
_.values(buttonStyle),
active ? buttonStyle.active : buttonStyle.inactive,
]}>
<Text sstyle="fs-14 lh-20 ff-me">{radioItem.label}</Text>
</View>
</TouchableOpacity>
</View>
);
})}
</View>
</>
);
};Overuse of Redux for managing simple UI and navigation states caused bloated reducers, tight coupling, and limited scalability.
Solve the limitations of a Redux-only architecture by establishing a more flexible state management model. It needed to support different state scopes without losing consistency, clarity, or performance across the app.
To separate concerns between global, local, and transient state, I introduced React Context for feature-specific flows and navigation parameters for transient data transfers. To keep the team aligned, I documented the new conventions, built typed navigation helpers, and refactored existing modules to follow the new layered structure.
Below is an example showing how a screen combines Redux, Context, and navigation parameters to manage all states in an organized way.
import { useRoute } from '@react-navigation/native';
import React, { useContext, useState } from 'react';
import { useSelector } from 'react-redux';
export const ProfileScreen = () => {
const route = useRoute();
const { biometricsEnabledRoute } = (route?.params || {}) as any;
const [biometricEnabled, setBiometricEnabled] = useState(!!biometricsEnabledRoute);
const context = useContext(...);
const profile = useselector(...)
const handleOnPressBiometrics = async () => {
const newValue = !biometricEnabled;
setBiometricEnabled(newValue);
biometrics.toggle(newValue);
if (!newValue) return;
modal.warning({
context,
...
description: `Hi ${profile.name}...`,
buttons: [
...
],
});
};
return (
...
);
};
Unavailable due to development constrained on the company internal environment.
The project faced an aggressive 6-month deadline with 30+ frontend engineers working across multiple projects (cards, ATM, onboarding, payments, profile). Each team maintained a separate codebase, yet all merged into a single release pipeline. The compressed schedule and overlapping workstreams created a chaotic environment of frequent overwrites, lost commits, unstable builds, and ongoing issues during quality assurance.
Define and implement a reliable integration and release model to restore environment stability. The goal was to reduce merge conflicts, prevent code loss, and ensure predictable releases while meeting the delivery deadline.
I started by breaking down the entire release
process from the top. To understand why the builds kept
failing, I reverse engineered the pipeline, tracing it
from the App Store approval steps all the way back
to the developer commits.
Once I mapped the flow, I updated the Jenkins
configuration to build only from predefined tags. Since
there was no versioning strategy in place, I introduced
a manual semantic release process that triggered deployments
only when a new tag was created.
With the pipeline under control, I defined a new branching
model inspired by GitHub Flow and GitFlow,
extended to handle multiple environments.
After defining the model, I aligned with the
team leads about the new process, detailing how developers
should create branches, tag releases, and merge safely
into the shared pipeline.
Below is a simplified diagram of the branching and release model that
illustrates how the pipeline operated after those changes.
Under tight deadlines, each squad had its own designer and developers building features with no alignment across teams. Without a shared design source or centralized library, teams recreated the same components in different ways, leading to duplicated code, inconsistent visuals, and a fragmented user experience across the app.
Bring visual and structural consistency back to the product by creating a single source of truth for UI. I needed to align all squads around one shared component library that could live inside the company restricted environment and be easy for every team to adopt without slowing delivery.
I joined the design squad to understand the centralized work they were creating and took responsibility for bridging communication between designers and developers. After assessing each team’s workflow, I determined that the most effective way to standardize the UI was by creating a framework-agnostic, ready-to-use CSS component library instead of AngularJS components, allowing squads to use it in any context. I built the library with Sass for consistent styling and used KSS to document it with clear visual component references. To distribute it within the restricted network, I set up a bare Git repository on a shared internal resource, enabling all squads to pull updates directly into their projects.
Unavailable due to development constrained on the company internal environment.
With more than 90 million users and about 24% of the population living with some form of disability, a significant number of customers faced barriers using the app. The company had begun enforcing WCAG accessibility standards across all digital products.
As part of a cross-squad design system team, I was
responsible for ensuring the new Angular 7
components complied with WCAG 2AA accessibility
standards. The team was tasked to:
1. Integrate accessibility requirements into the design
system’s development workflow.
2. Validate accessibility with real users, supported by
a dedicated QA subteam composed of people with disabilities.
3. Guide external consultancy to accelerate
implementation and ensure technical alignment with accessibility best
practices.
The effort began by contracting and onboarding a 7-person consultancy team, integrating them into our workflow and setting up their environments to match the internal CI process. With the team established, collaboration expanded to the QA group of testers with disabilities, whose feedback guided accessibility refinements in real use cases. As the work evolved, our squad became the bridge between design and engineering, revisiting UI patterns and adjusting layouts, color palettes, and interaction models to meet WCAG 2AA standards. The development phase introduced ARIA roles, keyboard navigation, and color-contrast adjustments, ensuring full compatibility with NVDA, VoiceOver, and TalkBack. This transformed accessibility from a patch into a core design system feature. To close the cycle, the new practices were documented and distributed so future squads could maintain the same accessibility standards.
Unavailable due to research environment constraints.
The framework ran on a legacy Java Struts architecture with complex dependencies, Firebird databases, and distributed tools. Each contributor manually configured environments by himself, resulting in version drift, failed builds, and long onboarding times.
Design a reproducible environment that unified all dependencies, databases, tools, JUnit test automation, and SOA integration workflows across all machines.
Packaged the full research stack into a virtual machine image containing Java SDK, Apache Struts, Ant, JUnit, Firebird, and SVN integration. Embedded startup scripts to initialize the database, compile the framework, and deploy local web services. Consolidated all UML diagrams, documents, and guides inside the VM for self-contained reproducibility.
Unavailable due to development constrained on the company internal environment.
The insurance management system relied on a
Delphi-based PDF parser that converted files
into plain text and navigated fields using
company-made custom parse
functions.
For more than a decade, the team maintained this fragile
approach, a clear case of code ossification
that prevented simpler and more maintainable solutions
like regular expressions from being adopted.
Enable reliable automated extraction of data without breaking existing parsers.
After a few months adjusting the parser through the
company’s custom functions, I proposed
introducing regular expressions to simplify data extraction.
The idea faced initial resistance due to years of
code ossification and comfort with the old cursor
logic.
After repeated attempts and personal insistence, I
finally got approval to add the regex function to the
core parser. Once integrated, it quickly proved its
value, successfully parsing complex document sequences that
previously required extensive manual position tracking.
With those results, I was asked to help the team on
writing and maintaining regex-based extraction
rules for other document schemas, formalizing
pattern usage as the new standard for PDF parsing
within the system.