[{"data":1,"prerenderedAt":125},["ShallowReactive",2],{"project-\u002Fprojects\u002Fbri-anti-money-laundry":3,"project-surround-\u002Fprojects\u002Fbri-anti-money-laundry":114},{"id":4,"title":5,"body":6,"category":92,"client":93,"cover":94,"description":95,"extension":96,"featured":97,"meta":98,"navigation":99,"order":100,"path":101,"role":102,"seo":103,"stem":104,"subtitle":105,"tech":106,"year":112,"__hash__":113},"projects\u002Fprojects\u002Fbri-anti-money-laundry.md","BRI — Anti Money Laundry",{"type":7,"value":8,"toc":84},"minimark",[9,14,18,22,28,35,40,51,56,64,69,77,81],[10,11,13],"h2",{"id":12},"overview","Overview",[15,16,17],"p",{},"BRI's Anti Money Laundry program required reliable data pipelines that could pull from many source systems, stage and reconcile, then deliver clean data to AML detection workloads. The pipelines needed to be repeatable, observable, and aligned with the regulatory data model.",[10,19,21],{"id":20},"approach","Approach",[15,23,24],{},[25,26,27],"strong",{},"Data modeling",[29,30,31],"ul",{},[32,33,34],"li",{},"Designed staging tables in Hive to hold raw source data prior to processing.",[15,36,37],{},[25,38,39],{},"Pipelines",[29,41,42,45,48],{},[32,43,44],{},"Wrote Python scripts using Spark to integrate and transform data inside Hive.",[32,46,47],{},"Built automated ETL pipelines moving data from staging into AML target tables.",[32,49,50],{},"Tested and debugged each pipeline path end-to-end.",[15,52,53],{},[25,54,55],{},"Performance & observability",[29,57,58,61],{},[32,59,60],{},"Tuned Spark configurations and ETL stages for production throughput.",[32,62,63],{},"Implemented logging and monitoring around ETL activity so issues surfaced fast.",[15,65,66],{},[25,67,68],{},"Collaboration",[29,70,71,74],{},[32,72,73],{},"Worked alongside the AML team and stakeholders to make sure data semantics matched regulatory requirements.",[32,75,76],{},"Produced technical documentation covering processes, architecture, and configuration.",[10,78,80],{"id":79},"outcome","Outcome",[15,82,83],{},"Repeatable, monitored pipelines that AML analysts could trust — and that operations could troubleshoot without paging the original author.",{"title":85,"searchDepth":86,"depth":86,"links":87},"",4,[88,90,91],{"id":12,"depth":89,"text":13},2,{"id":20,"depth":89,"text":21},{"id":79,"depth":89,"text":80},"data","Bank Rakyat Indonesia",null,"Built automated ETL pipelines that move raw data through Hive staging into AML-ready target tables, with monitoring and documentation.","md",false,{},true,6,"\u002Fprojects\u002Fbri-anti-money-laundry","Data Engineer",{"title":5,"description":95},"projects\u002Fbri-anti-money-laundry","Hive + Spark ETL pipelines for AML data integration",[107,108,109,110,111],"Hive","Apache Spark","Python","ETL","AML","2023–2024","qoXdH08FPbyYbmcoUoFzgrPIRVT4SqeV-cPfRIsa2yE",[115,120],{"title":116,"path":117,"stem":118,"subtitle":119,"children":-1},"BPKD DKI Jakarta","\u002Fprojects\u002Fbpkd-dki-jakarta-maintenance","projects\u002Fbpkd-dki-jakarta-maintenance","Continuous Database Support and Maintenance",{"title":121,"path":122,"stem":123,"subtitle":124,"children":-1},"Bank Sahabat Sampoerna","\u002Fprojects\u002Fbss-database-upgrade","projects\u002Fbss-database-upgrade","Upgrading Oracle from 11g to 19c without breaking the bank",1780137749003]