Can you tell me a bit about yourself and your experience for this kind of role?
I am a mid-level backend developer with experience in Python services and data-heavy systems, which fits well with this type of role. I enjoy taking slightly messy business rules, like fees or regulatory rules, and turning them into clear, reliable code that is easy to maintain. I work comfortably in Unix-based environments with Git platforms, and I am used to code reviews and CI pipelines. I tend to be calm and systematic, and I like to confirm my understanding in plain language before I start changing important logic.
For example, in a previous role I extended a core Python fee-calculation service after product raised issues about mis-priced edge cases. I met with the product owner and analyst, restated the problem in my own words, then refactored the code so the fee rules were in a clear function and configuration structure. I added focused unit tests for the edge cases and ran the full test suite in our Unix-based CI, then responded carefully to review comments. As a result, the mis-pricing stopped and support tickets dropped to zero, and a teammate later added a new fee type just by updating the configuration. In another project, I added data quality checks for regulatory reporting and linked each check back to the relevant rule, which made it easier for compliance colleagues to trust and understand the system.
(Built from a mix of your stories.)
What would you say are your main strengths for this role?
My main strengths for a backend role like this are careful thinking about rules, clear structure in code, and steady collaboration. I am good at taking complex business or regulatory requirements and breaking them into simple, testable conditions. I also focus on maintainability, so that other developers can extend the logic later without fear. This leads to fewer hidden edge-case bugs and makes it easier for teams to work together on the same code.
For example, when I improved the Python fee-calculation service, I did not just patch the bug. I proposed extracting the fee rules into a separate function and configuration, wrote clear unit tests for risky scenarios, and documented the approach in the GitHub ticket. That change fixed the mis-priced transactions and allowed a teammate to add a new fee type later without touching the core logic. In another project, I rewrote regulatory rules into simple if/then statements, checked them with an analyst, and then coded validations with clear error messages, which helped the compliance team review issues much more easily.
(Built from a mix of your stories.)
What is a development area or something you find difficult, and how are you working on it?
One development area for me has been thinking through all the edge cases when I change shared utilities, especially around dates and time zones. In the past, I focused on the main use cases and existing tests, and I did not always imagine how every other service might be using the same library. I am working on being more systematic and cautious when I touch code that many teams depend on. This helps reduce the risk of subtle bugs that only appear in certain environments.
For example, I once changed a shared Python date library, ran the existing tests, and everything passed, so we merged it. A few days later, a teammate found their service was returning slightly incorrect dates for one time zone, and we traced it back to my change. I took responsibility, sat with them to understand their usage, wrote a focused test to reproduce the bug, and realised I had not considered both naive and timezone-aware datetime objects. I updated the library to handle both safely, added more unit tests around time zones, and suggested a checklist item in our review template for date and time edge cases. Since then, we have avoided similar issues, and I am more deliberate about thinking through edge cases in shared code.
(Built mainly from your “something went wrong” story.)
Why are you interested in this role and working with our organisation?
I am interested in this role because it focuses on backend work where clear rules, data quality and reliable services really matter. I enjoy roles where I can take ownership of a part of the system, keep it stable, and improve it gradually with the team. From what I understand about your organisation, you value careful engineering, collaboration and long-term maintainability, which matches how I like to work. I am also drawn to environments where product, analysts and developers talk openly about requirements and edge cases.
In my previous roles, I have enjoyed similar work. For example, I owned a small but important risk-score API that other services depended on, and I quietly kept it healthy while supporting a junior developer to contribute safely. In another project, I worked closely with compliance to turn regulatory rules into clear validation scripts, with comments linking back to the original document. In both cases, the impact was fewer production issues, clearer processes and easier collaboration across teams. I would like to bring that same steady, structured approach to this role with your organisation.
(Built from a mix of your stories.)
Can you tell me about a time when something went wrong and how you handled it?
There was a time when I changed a shared Python library for date calculations and a subtle bug reached production. I had run the existing unit tests and everything passed, so I merged the change after review. A few days later, a teammate noticed their service was returning slightly incorrect dates for a specific time zone, and we traced it back to my update. The impact was limited but it showed that I had not fully considered all edge cases.
As soon as we realised, I acknowledged that my change caused the problem and offered to lead the fix. I sat with my teammate to understand exactly how their service used the library and wrote a small test that reproduced the bug. I then reviewed my assumptions and saw that some services passed naive datetime objects while others used timezone-aware ones. I updated the library to handle both safely, added more unit tests around time zones, and wrote a short note in the documentation about expected input. I also suggested adding a checklist item in our code review template for date and time edge cases. We deployed the fix the same day, the incorrect dates stopped, and the new tests and checklist helped us avoid similar bugs later.
(Built mainly from your “something went wrong” story.)
How do you handle stress, for example when a deadline changes or the workload increases?
When stress increases, I try to stay calm and make the work more structured. I usually start by listing the tasks, checking dependencies, and then agreeing priorities with the team or manager. I like to communicate clearly about what is realistic, what can be trimmed, and where there are risks. Breaking the work into smaller steps and focusing on one thing at a time helps me stay steady and avoid mistakes.
For example, when I was the main developer for a risk-score API and a junior colleague joined, we had to enhance the API while also working towards a related deadline. I managed this by giving them a clear walkthrough, setting them up with tests and debugging in VS Code, and reviewing their merge requests with specific, kind feedback. At the same time, I kept the project manager updated in plain language about progress, edge cases and any small risks, and I raised a growing response-time issue early with a plan to fix it. This structured approach meant we delivered the API changes on time, fixed the performance issue before it became an incident, and the junior developer grew in confidence instead of feeling overwhelmed.
(Built mainly from: Owning your area and helping others.)
Can you tell me about a time you had to manage several tasks or deadlines at once?
I had to manage several tasks at once when I was responsible for a risk-score API that other services depended on, while also supporting a new junior developer and meeting a project deadline. There were feature enhancements to deliver, performance issues starting to appear, and onboarding tasks for the new colleague. To handle this, I prioritised the work based on risk and impact, and I made sure communication with the project manager was clear and regular. This helped avoid surprises and kept the workload manageable.
In practice, I started by giving the junior developer a focused walkthrough of the API and tests so they could contribute safely. I reviewed their merge requests carefully, left specific comments on naming and error handling, and paired with them in VS Code to show debugging and test running. At the same time, I monitored the API logs and noticed response times slowly increasing, so I raised it early and suggested a small refactor and index changes that we could fit around the main deadline. By planning that work and keeping stakeholders informed, we delivered the changes on time, fixed the performance issue before it became a production incident, and helped the junior developer progress to more complex tasks.
(Built mainly from: Owning your area and helping others.)
Can you tell me about a time you worked closely with someone else to get something done?
I often work closely with others, especially analysts, product owners and other developers, to make sure we all understand the same problem. I find that restating requirements in simple language and checking them together reduces confusion later. I also like pairing sessions when they are focused and time-boxed, because they help share knowledge and catch issues early. This kind of collaboration usually leads to clearer code and fewer support issues.
For example, when I extended the core Python fee-calculation service, I first met with the product owner and a business analyst to walk through real mis-priced examples. I restated the problem in my own words to confirm my understanding, then proposed a small design change and explained it in a short design comment on the GitHub ticket. After implementing the change, I responded calmly to review comments and adjusted naming and documentation where reviewers suggested clearer options. In another case, when adding regulatory data checks, I walked through my if/then interpretations of the rules with the analyst and emailed them about a conflict I spotted. In both situations, this close, clear collaboration meant we caught issues early and delivered solutions that colleagues could trust and extend.
(Built from a mix of your stories.)
Can you give an example of how you learnt a new system or process and became confident using it?
When I join a new system or process, I like to build a simple mental model first, then deepen it with examples and tests. I usually start by reading any existing documentation, then I trace through the main flow in the code and write down a simple diagram or notes. I also find it helpful to restate the rules or behaviour in my own words and check that with someone who knows the system well. This approach helps me become confident and reduces mistakes when I start making changes.
For example, when I was asked to extend the Python fee-calculation service, I treated it like a new system to learn. I read through the existing module and drew a simple diagram of the fee calculation flow, including where configuration and constants were stored. I then met with the product owner and analyst to walk through real mis-priced examples and restated the problem in my own words to confirm my understanding. After that, I proposed a small design change, implemented it on a feature branch, and added unit tests for edge cases and risky scenarios. As a result, I became confident working in that core service, and the changes reduced mis-pricing issues and made future updates easier for the team.
(Built mainly from: Building and improving the main product.)
Can you tell me about a time you disagreed with a colleague or stakeholder, and how you resolved it?
When I disagree with someone, I try to focus on the facts and the shared goal rather than on who is right. I like to restate my understanding of their view, then explain my concern using clear examples or data. If needed, I ask questions to clarify the intent behind a rule or request, instead of assuming. This usually turns a disagreement into a joint problem-solving discussion and leads to a clearer, safer solution.
For example, when I was adding new data quality checks for regulatory reporting, I noticed that two of the rules in the document seemed to conflict for restructured loans. Instead of just picking one interpretation, I wrote out both as simple if/then statements and prepared concrete data examples. I then emailed the analyst and compliance contact, explaining the potential conflict and asking which interpretation matched the regulator’s intent. We discussed it and agreed on the correct behaviour, which I then implemented in the validation scripts. By handling the disagreement in a calm, evidence-based way, we avoided misreporting, and the compliance team found it easier to trust and review the checks because they were clearly linked back to the agreed rules.
(Built mainly from: Using finance and rules knowledge in code.)