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l, I initially built automation scripts for trade reconciliations and reporting using standalone Python and UNIX-based cron jobs. These worked well for smaller data volumes, but as our operations scaled across global teams in London, Hong Kong, and the Bahamas, performance and maintainability became a bottleneck.

I realized I needed to adapt my workflow to better support large-scale, distributed processes. So, I changed how I worked by migrating critical pipelines from PostgreSQL and flat scripts into a more robust ETL framework using Databricks and Delta Lake. I restructured batch processes into streaming jobs and modularized the Python code to be production-grade. I also started using Kafka and Kubernetes to improve system reliability and orchestration.

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.

Situation:
While working on the CloudGate (CASB) program at Oracle, I had to collaborate closely with a lead engineer who was highly technical but often resistant to input from non-engineering stakeholders, including program managers like myself.

Task:
Our shared goal was to integrate CloudGate’s policy engine with a third-party SaaS provider under tight deadlines for a compliance milestone. Coordination was critical, but communication friction risked delaying delivery.

Action:
I focused on building mutual respect by first acknowledging his technical expertise and then shifting our interactions to be data- and goal-driven. Instead of pushing process, I came to discussions with clear risk/impact scenarios and timelines tied directly to customer and audit deadlines. I also adapted my style—fewer meetings, more async updates with technical clarity—which matched his preference. Gradually, we established a working rhythm built on outcomes, not personalities.

Result:
We delivered the integration on time, passed the compliance audit, and by the end of the project, had built enough trust that he started proactively surfacing risks to me before I had to ask. The experience reinforced for me that adapting communication style and focusing on shared goals is often more effective than trying to change personalities.

joins perform better than subqueries as the subqueries will need to hold a lot of data in the memory

I recently took over a team from a different tribe, my approach was to observe what the team was doing and how I could best help them. 

One of the observations was that a team member was not contributing as much as was expected and ultimately was underperforming. I raised this with the previous line manager and he validated my concerns and said that he will likely get another developing in performance review and be put on a PIP. 

After some initial conversation with the individual, discussing what I've observed and how his not meeting expectations, I was pretty sure, this person was checked out and was likely heading for a difficult performance review

However, after some further discussion and people a relationship with that individual, they opened up and start to share their difficulties they were having. This then allowed me reach out the necessary teams to get them the support they needed. 

Utlimately, they proved me wrong, as due the the hard work with both put in, his on track for a good performance review 

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