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In my role leading the launch of that new customer onboarding system, the pressure was really on. We had that tight three-month window and a lean team to build something that would be the first impression for all our new users. One specific hurdle we faced was integrating our legacy customer database with this brand-new system. The data structures were quite different, and initially, the data migration process was proving to be much slower and more error-prone than anticipated. This threatened to push our launch date.

To tackle this head-on, I didn't just delegate. I dove into the technical details with the lead engineer. We mapped out the data fields meticulously, identified the key transformation rules needed, and actually prototyped a more efficient data migration script using Python. This allowed us to automate a significant portion of the process that was previously manual.

Furthermore, to ensure we were building the right thing quickly, we implemented very short feedback loops. We'd build a small piece of the onboarding flow, get immediate feedback from a small group of internal users, and iterate based on their input. For example, early on, users found a particular step in the registration process confusing. Based on their feedback, we completely redesigned that screen within a couple of days, leading to a much smoother experience.

By getting into the technical weeds to optimize the data migration and by relentlessly focusing on user feedback through rapid iterations, we not only overcame the risk of a delayed launch but also delivered an onboarding experience that was significantly more user-friendly than initially envisioned. It was incredibly rewarding to see new customers move through the system so smoothly right from day one. That success really underscored the power of combining technical problem-solving with a user-centric approach

At my previous role, I was leading a fraud detection AI model deployment for a financial services client. The model aimed to reduce fraudulent transactions by 25% while maintaining a low false positive rate.

I was confident in the model’s performance in the test environment, but when we deployed it in production, we noticed an unexpected spike in false positives. Transactions flagged as fraudulent were legitimate, causing disruptions for customers and increasing manual review work. To resolve this, I:

1. Investigated the issue by comparing training and production data.
2. Identified data drift—the model was trained on historical data, but recent transaction patterns had changed.
3. Worked with data engineers to implement real-time data monitoring.
4. Retrained the model with up-to-date data and adjusted hyperparameters to improve generalization.

This fix reduced false positives by 25%, improved fraud detection accuracy, and ultimately saved $500M in operational savings across multiple initiatives. This experience taught me the importance of continuous model monitoring and proactively accounting for data drift. Now, I always incorporate data validation pipelines and feedback loops into AI deployments to prevent similar issues.

As my last semester as a system engineer student, I went above and beyond on time management of the time of the team and mine, this was on my thesis as student. while we where a team of 4 members, one of our members and I had one of our parents fighting cancer, so we needed to do time management to work in the project and have a correct time management of personal and work time. What I did was stated my focus, helped my companion doing the same noticing in what of the project we where helping the most and focus on that on my case the SQL queries & documentation of the project. Propose the solution to the other members, while also covering more and more in the written part of the project, this solution was applied by me, but i also had to cover sometimes my team member, doing what was also his tasks in this case the design of the project processes, most of the UML diagrams, because the situation of his family member worsen and some one needed to pick up the pace of the project and that person was me

From my perspective, to get cross functional teams working together it is key to communicate clearly, work efficiently and build trust.

One of the first steps to working with cross-functional teams is to define clearly roles & responsibilities. This helps to avoid confusion, duplicated tasks or conflicts of work. Also helps team members to stay focus on their tasks.

Set manageable project goals is also key for cross-functional collaboration. Define SMART goals help team members to understands the objectives, expectations, and timelines.

Communication is essential for any team, but especially for cross-functional teams, who may have different backgrounds, languages, or styles. So clear and direct conversation with team members, asking questions, clarifying doubts to keep everyone on the same page. à Tools: regular meetings, email, Slack, repositories & Jira

Providing visibility to the stakeholders helps to keep updated regarding project progress to all parts involved. Understanding progress in real-time enables teams to adapt fast to changes and readjust strategy if needed.

Build trust among team members is also key. PMs must be honest and transparent with team members to build credibility. Also encouraging team members have open discussions during core team meetings and express their ideas openly with respect is an important part of collaboration between teams.

I graduated in May 2024 and recently wrapped up my role at Bright Mind Enrichment. It was a great experience, and I stayed through my agreed-upon commitment.  At this point, I’m looking for a full-time opportunity that offers deeper ownership, long-term growth, and technical mentorship—especially in a full-stack environment like Atlassian’s.