Product R&D — configuration and validation systems

Backend product engineering in a mature Java platform, working across configuration, business rules, pricing, validation, automated testing, and integration environments.

Working inside a mature product system

I joined CSG in Product R&D, working on backend services behind product configuration, quoting, pricing, and validation.

Business behavior in this platform rarely belongs to a single component. A configuration request may pass through different representations, apply rules based on the current product state, retrieve data from other services, and require consistent behavior across multiple API workflows.

That makes understanding existing behavior an important part of implementation. A small-looking change can affect several paths, while a seemingly large feature may already be supported by most of the system.

My work has gradually expanded from individual features and regression testing into more complex rule processing, performance investigation, shared test infrastructure, and technical review.

Extending validation into rule application

One of my largest contributions involved extending an existing cross-specification validation system to actively apply business rules during configuration.

Previously, the configuration APIs could construct product relationships and attributes from defaults and overrides, while several cross-specification rules were primarily evaluated during validation. This left a gap between what a configuration should contain and what the APIs actually returned.

I designed and implemented a shared rule-application framework supporting both quote-item and order configuration. The implementation applied relationship and characteristic constraints, updated nested configuration structures, and reevaluated rules as configuration state changed.

The complexity came from interactions between rules rather than individual conditions. A rule could introduce a new component, alter the available attributes, or trigger further evaluation in a nested context. The implementation needed to preserve existing configuration behavior while handling those changes consistently.

The framework evolved through technical discussions, code reviews, and several rounds of simplification. The experience reinforced the importance of separating responsibilities, controlling recursive behavior, and being deliberate about when additional abstraction is justified.

Delivering features across configuration boundaries

Alongside the larger rule-application work, I implemented backend enhancements involving quantity handling, usage-based and site-dependent pricing, agreement detection, rule-evaluation contexts, and stage-aware relationship validation.

These changes often required tracing business behavior across several layers. For example, increasing a configured quantity should not automatically multiply a charge that represents a per-unit usage rate. Similarly, adding location-dependent configuration can change how prices and charge groups must be represented in an API response.

The Tariff Matrix work was another example. Pricing already supplied much of the required information, so the task was to identify where the configuration preview was losing or omitting that data and preserve the relevant charges and rate tables without duplicating pricing logic.

The recurring challenge was not simply adding another condition. It was finding the component responsible for a behavior, understanding what downstream consumers expected, and making the smallest coherent change.

Correctness beyond the initial implementation

Complex configuration behavior also exposed regressions involving relationship cardinality, nested component identities, attribute limits, collection children, and product offering overrides.

I investigated and fixed several of these defects, including issues discovered during subsequent integration testing and a customer-reported configuration problem.

The fixes were accompanied by additional unit and Karate end-to-end regression tests. These covered interactions across both quote-item and order configuration, particularly cases where behavior depended on nested structures or multiple constraints being evaluated together.

As the test suites grew, their setup and teardown logic became increasingly repetitive. I later refactored duplicated catalog preparation into reusable shared workflows while keeping the actual API assertions separate.

This work also involved walking QA through test changes and regression scenarios. It made the distinction between implementing a feature and making that feature reliably testable much more concrete.

Making shared test environments usable

Some engineering problems were outside the application logic itself.

I worked on integrating connectivity product reference data into shared testing environments, where inconsistent identifiers, missing dependencies, and differences between solution configurations could prevent realistic testing or deployment.

The work involved reconciling specification and reference data, preparing repository-managed configurations, and addressing deployment-blocking dependencies. I also worked with Kubernetes-based environments and validated deployment behavior through a local development stack.

The interesting part was the relationship between application data and infrastructure. A service could be running correctly while the product configuration required to exercise it remained invalid or incomplete.

Resolving those issues required treating configuration data, deployment dependencies, and the testing environment as parts of the same system.

Investigating performance before optimizing

I also investigated an expensive validation request path using load testing and Java profiling.

The initial suspicion concerned repeated serialization and deserialization. Instead of immediately changing the conversion code, I profiled the execution path to identify which operations were responsible for the observed cost.

The investigation revealed significant work in inbound XML processing through the framework’s binding layer, alongside rule-evaluation and data-access overhead. An important distinction was that the inbound framework path was not using the same converter as other parts of the application.

I documented the findings and proposed follow-up work around XML conversion, rule-evaluation overhead, redundant data transformations, and validation-pipeline processing.

These were investigation findings and proposed remediations, not completed performance improvements. The value of the work was establishing a clearer technical basis for deciding what to optimize.

Engineering judgment through technical review

Code review became another way I contributed beyond my assigned implementation work.

I reviewed changes in backend validation and an AI-assisted service, covering correctness, edge cases, error handling, API contracts, and design maintainability.

Some discussions concerned structural simplification: introducing clearer abstractions, reducing unnecessary coupling, improving composition between components, or reusing information retrieved earlier in an execution flow rather than fetching it repeatedly downstream.

These were opportunities to reason about the underlying design rather than only the immediate code change.

They also reinforced an important lesson from my own implementations: a technically correct solution is not necessarily the simplest solution to maintain, extend, or explain.

The through-line

The most consistent part of my work has been learning to distinguish where a problem appears from where it actually originates.

Sometimes that means tracing a rule through several representations. Sometimes it means discovering that a pricing capability already exists and only its downstream presentation is incomplete. Sometimes it means finding that an apparent application issue comes from reference data or deployment dependencies.

Increasingly, it also means examining the structure of the solution itself: whether responsibilities are divided clearly, whether data is retrieved unnecessarily, whether tests can be reused, and whether an implementation introduces more complexity than the problem requires.

That is the direction in which this role has developed my engineering practice: from understanding and implementing individual changes toward reasoning about the wider system and the trade-offs that shape it.

Technologies and areas

Java · Backend APIs · Business rule processing · FEEL · Jakarta/JAXB · JUnit · Karate · Kubernetes · AWS EKS · k6 · async-profiler · Performance investigation · Code review