[{"data":1,"prerenderedAt":101},["ShallowReactive",2],{"case-study:\u002Fcase-studies\u002Fpricing-rules-engine":3},{"id":4,"title":5,"body":6,"date":90,"description":91,"extension":92,"meta":93,"navigation":94,"path":95,"provenance":96,"sensitivityReviewed":90,"seo":97,"sources":98,"stem":99,"__hash__":100},"caseStudies\u002Fcase-studies\u002Fpricing-rules-engine.md","A Pricing Rules Engine for Large Portfolios",{"type":7,"value":8,"toc":79},"minimark",[9,14,18,22,25,29,32,36,39,43,46,50,72,76],[10,11,13],"h2",{"id":12},"problem","Problem",[15,16,17],"p",{},"Pricing decisions across large merchant portfolios at Heartland Payment Systems\nwere complex and data-heavy. Users needed a repeatable way to evaluate repricing\nscenarios and make data-informed pricing decisions against current portfolio\ndata, rather than working through the decision process by hand each time.",[10,19,21],{"id":20},"constraints","Constraints",[15,23,24],{},"This was a production application with real-time pricing capabilities, evaluating\nscenarios across millions of rows of portfolio data, so both correctness and\nperformance mattered. Access had to be controlled and verified, and the pricing\nlogic had to stay legible and maintainable as business rules evolved.",[10,26,28],{"id":27},"architecture","Architecture",[15,30,31],{},"I architected and developed major portions of the full-stack pricing\napplication, contributing to both frontend and backend architecture. I helped\ndesign a rules engine used to evaluate repricing scenarios across millions of\nrows of portfolio data, and built user-facing workflows for reviewing pricing\nresults and applying business rules. I integrated application access with Google\nSSO and data-access verification, and worked to improve performance and code\nlegibility in the complex pricing logic. I collaborated with product managers and\ndata engineers to translate pricing requirements into maintainable application\nbehavior. I also contributed to CI\u002FCD workflows in Azure DevOps, infrastructure\nchanges in Terraform, application logging, monitoring, rate limiting, and\nproduction troubleshooting.",[10,33,35],{"id":34},"tradeoffs","Tradeoffs",[15,37,38],{},"Encoding pricing decisions in a rules engine adds indirection compared with\nbespoke logic per scenario, and a rules layer has to be evaluated across millions\nof rows fast enough to feel real-time. I accepted that indirection because a\nmaintainable, repeatable rules engine was worth more over time than\nscenario-specific code that would be harder to evolve as pricing requirements\nchanged.",[10,40,42],{"id":41},"testing","Testing",[15,44,45],{},"I focused on improving the performance and legibility of the complex pricing\nlogic, and supported the application through CI\u002FCD workflows, application\nlogging, monitoring, and production troubleshooting, so behavior could be\nverified and diagnosed against real usage.",[10,47,49],{"id":48},"results","Results",[51,52,53,62,67],"ul",{},[54,55,56,57,61],"li",{},"Replaced portions of a complex, data-heavy decision process with a repeatable application workflow ",[58,59,60],"span",{},"docs\u002Fprofile\u002Fprojects.md",".",[54,63,64,65,61],{},"Enabled users to evaluate pricing decisions across large portfolios using current data ",[58,66,60],{},[54,68,69,70,61],{},"Created a more maintainable foundation for evolving pricing rules and business requirements ",[58,71,60],{},[10,73,75],{"id":74},"what-id-change-and-what-i-learned","What I'd Change and What I Learned",[15,77,78],{},"Building a rules engine over portfolio data taught me how much the maintainability\nof the rules themselves matters. Next time I would invest earlier in making the\nrules easy to read, test, and change in isolation, since that legibility is what\nlets a pricing system keep pace with the business rather than ossify around its\nfirst set of requirements.",{"title":80,"searchDepth":81,"depth":81,"links":82},"",2,[83,84,85,86,87,88,89],{"id":12,"depth":81,"text":13},{"id":20,"depth":81,"text":21},{"id":27,"depth":81,"text":28},{"id":34,"depth":81,"text":35},{"id":41,"depth":81,"text":42},{"id":48,"depth":81,"text":49},{"id":74,"depth":81,"text":75},"2026-07-22","Architecting a full-stack pricing application and rules engine that evaluated repricing scenarios across millions of rows of merchant-portfolio data.","md",{},true,"\u002Fcase-studies\u002Fpricing-rules-engine","professional",{"title":5,"description":91},[60],"case-studies\u002Fpricing-rules-engine","HVp_DW0CIfgnDXw1AVktlHowCOTZrJBzLKZJlhIBOpU",1788403975816]