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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.

During final stages of testing upgraded version of core banking applications, we encountered a defect which required resolution from development team. After dev deployed the defect, I had very limited time to thoroughly retest and perform regression.

My task was to perform retesting and make sure there are no major defects after the release.

After resolution, based on my prior knowledge of the apps I grouped those testcases that had a potential impact to run automation regression to speed up the testing. While I manually tested the defects with some edge case scenarios. 

I kept the cross functional team updated about the testing progress and the risk involved if testing wasn't completed on time. 

But, I was able to mitigate the risk of not completing the testing on time and major risk of unexpected issues with the help to prior knowledge and proactive communication.

Thus, I learnt from this incident to carefully strategizing the situation and collaborative approach can help to mitigate risks.

I try to understand their point of view and be open to new ideas. If I listen without being defensive I know  the other party will also listen. That way we can take the best idea from both and come up with great solution.

One project i'm proud of is my internship project which was a full stack project where i had the chance to create a prototype of a template manager , storing the data into a cassandra database, alongside using springboot and java to work on the module structure and life of the project, I then used js and react for UI. This prototype was then deployed and is now being used by internal payment team at jpmc, instead of going to search every resource, i store all the templates in one template manager and they can know just search the nav bar and get the exact template they need. I'm proud of this project not only because it was my first real project using the new languages and frameworks i was learning during internship but it also gave me the confidence of knowing and seeing that I can do this even though i dont have a degree in this field!

In my last role I had to make a UX decision about the product that will balance functionality for the user without compromising performance. After testing a few options and validating it with internal stakeholders I released the updated UX, but then the CEO (who was a heavy user of the product) was against that change. I saw down with him and explained my reasoning and the process that I took to validate the solution, and gave him the option to try it himself on an early version. Once he saw the big picture he agreed that my solution was the right approach, but asked that next time I will consult with him before releasing changes like this. 

Thank you for the question. I have been working as a data engineer for a combined total of three years, working closely with business leaders and engineers at different enterprises. where my main focus and speciality was in building reliable data pipelines that supports business decision making. I also have experience running a solo consultancy firm, where I have had the opportunity to consult and build end to end data products for businesses, which has allowed to be explore and hone my leadership, and communication skills on top of my technical expertise. Above all. in addition to the skills  I have learnt over the last three years, my experience in academia, especially as a consequence of my PhD research, I have a strong preference and a proven track record  in delivering complex analytical tasks.  All of the above essentially package me up as  an ideal candidate for this role.

Earn Trust by:

1. Listen to the team, understand what outcomes they are seeking.

2. Figure out what value you can deliver for the team and produce an actual deliverable

3. 
   

1. Situation: Our cloud bill had been steadily increasing, but engineering teams lacked visibility into which workloads were driving the cost. Leadership wanted accountability, but SREs like me knew that finger-pointing wouldn’t solve anything without data.

2. Task:I wanted to design a way to give each team ownership of their spend — without adding operational overhead — while also uncovering unused or over-provisioned infrastructure.

3. Action:I built an internal “cost dashboard” that pulled billing data from our cloud provider’s API, tagged resources by team and environment, and visualized it by service, team, and project. But the innovative part was integrating it with our CI/CD workflows: before a PR deploying new infra could be merged, it would estimate the monthly cost impact. I also built a Slack bot that sent weekly team-level cost summaries and flagged idle resources.

4. Result: Within two months, teams identified and shut down $8,000/month in unused dev resources. More importantly, it shifted the culture — infra cost became part of architectural discussions. Finance even started using our dashboard during quarterly planning instead of relying solely on billing exports.

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.

I was working on a high stake project with very high visibility from my senior management.

Me and my manager were thinking of can we leverage the quality of the development using help from QA team. He wanted to keep the QA team separate. His point of view was that a separate team would keep clear focus for the team and they would focus on finding more customer facing bugs. I thought that we should have QA embedded in the scrum team as that would help in easier collaboration and quicker bug fixes plus this is a more efficient way. Though I understood his point of view I always believed that in QA embedded inside the scrum team. I went back had a thought to measure the merits and demerits of the proposals. I put down in a slide the merits and demerits of each. Merit from having a separate team, clear focus and better filtering of quality, demerits less collaboration with development team. I then proposed that the QA engineers attend the calls of dev teams but keep a separate QA board for their tasks. This will give clarity of what bugs they find, what test scenarios they create and finally the metrics to measure the bugs reported per development. My boss and I both agreed as it solved the problems of each proposals