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For prioritizing the product backlog, I evaluate each product across the dimensions of 1) impact on metrics/organization level goal the needle which it is trying to move, 2) reach of product as to how many personas or stakeholders experience will it improve 3) technical feasibility or complexity of the product being delivered which also includes dimensions of clarity in requirements and 4) if its of strategic importance to future growth

The last conference I attended was the Open Source Security Foundation. As I was leading the Dart & Flutter security program I wanted to learn about the innovations, tooling and services that other open source projects were using.

There were interesting conferences for tooling to sign artifacts with a single command and how the open source community was working in tooling to build, sign and release artifacts in a secure way for everyone using GitHub.

I learned that there were many technical innovations that I could use to speed up the Flutter Security Program, for example implementing secure build workflows using github actions. I also met several people working on similar projects and took the opportunity to share our lessons learned and challenges. Later on the connections with the open source security foundation proved very useful during technical reviews and collaborations.

while reviewing one of the older projects that was currently implemented in the company I noticed that there was no alerting in place if the data pipeline breaks, the data pipeline has never been broken and it never failed for the last 2 years. it was a pipeline where the data would be dropped into an sftp bucket by the customer every hour and then it was moved to an s3 bucket where s3 triggers were implemented to moved the data in to queue. So in case there is an issue with the data not being processed we would only know when the we see a drop in volume. I identified it as a potential issue with the system and worked on a proposal to have monitoring in place so we are notified when there is an error. I worked with the program manager and devised a plan on how to implement the alerting and the time lines as well as the effort that will be needed to implement it. I mapped the impact of not having it to the current goals of the company and then put forward a proposal of implementing the alerts to the business and took their buyins. after getting the buyins I proposed the task to my team and some of the sr devlopers were not too keen on implenting the alerts, I gave the task to some of the Jr devlopers and the alerts were implemented. after the implementation we discovered that for some hrs in a day we werent getting any files so instead of getting 24 files in a day we were getting 20 files a day in an average. This was rectified by talking to the customer and the missing data was solved. but this made the whole team realize the importance of monitoring and alerting. It also bosted the moral of the Jr developers seeing the direct impact they made to the business goals

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'd like to share a personal anecdote about a time when I failed to reach a metric as a Manager in a Data Engineering project.

Metric: Reduce data ingestion latency to under 10 minutes within 3 months to improve real-time analytics capabilities.

Context: Our team was responsible for building a data pipeline that ingested large volumes of data from various sources into a cloud-based data warehouse. The data was then used to power real-time analytics and reporting for our business stakeholders.

Goal: Our goal was to reduce the data ingestion latency to under 10 minutes to ensure that our analytics capabilities were as close to real-time as possible. This would enable our stakeholders to make informed decisions quickly and respond to changing market conditions.

Plan: Our plan was to optimize the data ingestion process by:

Improving the efficiency of our data processing algorithms

Increasing the scalability of our cloud infrastructure

Implementing a more efficient data storage solution

What went wrong: Despite our best efforts, we were unable to meet the metric. We encountered several issues that slowed us down, including:

Underestimating the complexity of the data processing algorithms, which required more development time than anticipated

Difficulty in scaling our cloud infrastructure due to vendor limitations and cost constraints

Unexpected issues with our data storage solution, which required additional debugging and testing

Impact: Missing this metric had a significant impact on our project. Our stakeholders were not able to get the real-time analytics they needed, which delayed their decision-making and affected our business outcomes.

Lessons learned: This experience taught me several valuable lessons, including:

The importance of robust planning and contingency planning to account for unexpected setbacks

The need for more accurate estimation of development time and resources required for complex tasks

The value of prioritizing and focusing on the most critical components of the project to ensure success

Actions taken: To address these issues, we:

Revised our estimates and created a more realistic project plan

Broke down the task into smaller, more manageable components to improve development efficiency

Implemented additional monitoring and testing to ensure that our data ingestion process was stable and performant

By learning from our mistakes and taking corrective actions, we were able to eventually meet the metric and improve our data ingestion latency to under 10 minutes.

As a PL,
- Make a positive impact towards 3 critical business imperatives
- Gain team's and stakeholders trust
- Learn across portfolios to be able to innovate

Situation:

I was laid off from my first job just four months after joining due to budget constraints. This was a major setback for me as I had just started my career. Around the same time, my father suffered a heart attack, which required me to stay at home and take care of him while he was on bed rest for several months. The combination of losing my job and dealing with a family emergency left me feeling devastated, and I struggled to apply for new roles.

Action:

Despite these challenges, I remained determined to grow in my career. While caring for my father, I focused on upskilling myself:

• I attended mentor sessions on ADPList to gain industry insights.

• I reached out to experienced designers on LinkedIn for portfolio reviews and feedback.

• I enrolled in courses through Interaction Design Foundation (IxDF) to expand my knowledge.

• I continuously improved my portfolio and resume, incorporating the feedback I received.

I also actively networked with professionals, which helped me regain confidence and visibility in the industry.

Result:

My persistence paid off. Where I once struggled to pass resume screenings, I started receiving recruiter calls consistently. In fact, this very interview happened because I proactively connected with a senior designer, shared my portfolio, and received a referral for this role. This experience not only strengthened my portfolio and skills but also taught me the power of perseverance, networking, and continuous learning.

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.