Can you walk me through your process for prioritizing product features and making decisions on what to develop?

I typically look at impact metrics, complexity and cost of development and get agreement with Product Management and Users

We recently expanded our resource planning system. As new organizations came onboard we got requests for several new features. 

My framework to handle new feature request includes the following:

- evaluation if the feature is needed and if there is a viable workaround

    That tends to satisfy about 10-15% of requests.

    In recent example of resource planning system maybe 30% of requests were covered by existing functionality

- evaluation of impact:

    does the feature affect external users, what % , in what capacity (working with Product Management team)

    does the feature affect critical processes and does it have Severity1 impact on our ability to do critical processes

        If any of these answers are Yes, the feature is prioritized. Technical debt is included in this review as it can affect external customers or our ability to support critical processes.

First we review these critical features and allocate approximate capacity. After that we review non-critical impacting features. We prioritize based on next level impact, urgency, and support needed for XFN project delivery.

In case of resource planning tool it was needed for accomplishing financial forecast for 2025 for entire company. We investigated MVP of what is needed for the new teams and addressed it creatively- balancing user training and the new features build. That allowed us to double our user base with a minimum size team.

I successfully introduced several processes that significantly improved our engineering efficiency and product delivery outcomes. One of the key initiatives was optimizing our existing application architecture. By spearheading the migration to a microservices architecture using Golang and PostgreSQL, I was able to enhance scalability and improve flexibility, leading to a remarkable 50x increase in order processing capacity. This allowed us to accommodate more transactions without compromising performance.

Additionally, I implemented a robust logging and alerting mechanism using Elasticsearch and New Relic, which improved our system observability and incident response times. This overhaul reduced our mean time to recovery (MTTR) by approximately 40%, ensuring that we maintained a seamless user experience even during peak traffic periods. Quality assurance was another area where I enacted changes; I built a scalable automated testing framework utilizing Cypress, which accelerated our delivery cycles by reducing manual testing time and increasing test coverage.

The rationale behind these changes was to address the growing user base and the increased complexity of our operations. By both improving backend infrastructure and enhancing testing efficiency, we were able to deliver exceptional user experiences while balancing technical constraints and business needs. Furthermore, I actively participated in weekly leadership calls to align our technology roadmap with the company’s strategic goals, ensuring that our engineering efforts were consistently contributing to the broader vision of Zebpay.

I would first  gather information from the team on level of effort for all product features. From there, I would gather key stakeholders together to determine what would make a minimum viable product. The product features needed to meet minimum viable product would need to be accomplished first based on resource availability. Once minimum viable product is met, we would move on to items that would enhance the product while keeping enough time for quality testing of the product before product launch.

Situation:

We had a new product launch coming up and were required to build data pipelines for the new product and integrate this new product data into existing analytics and reporting products the business relied on.

Action:

I split up the delivery of the required data into 2 phases. One was MVP and the other post-MVP. The MVP program was designed to deliver on the most critical data points required. Criticality was established by determining data required for contractual and executive reporting.

Result:

Product data was integrated on time at product launch

At Auctane, as a EM my role was to ensure that my team has the right items prioritzed for the team to work on.

If the priority is not right, we might be working on either a wrong item or one which is blocked - both will not be good for the productivity of the team. My job was to involve the right folks so that decisions can be made faster.

In order to priortize I used a matrix - with Impact, effort and dependencies and we would score each feature requested against this - I would inovle PM and at least one Senior engieer - a feature is atomic but might have some dependencies - which was identified - we would then put then in 30/60/90 day bucket - from which sprints would derive.

In order to prioritize product feature and decisions, I would look for the following 1. What is the business problem being solved? 2. Being customer obsessed, How does it improve the user experience?  Are their alternatives for customers. 3. What is the return on investment on effort vs business entitlement. This would be across business units/groups that depend on this feature. 4. Are their regulatory, safety, security and compliance mandatory aspects to be considered