Bellevue, Washington, United States
As a Senior Software Engineer at Siemens, I specialize in data engineering with a focus on optimizing real-time and historical data pipelines. Recent achievements include migrating Databricks streaming processes to a Redis-Postgres architecture and designing a data model to enhance vehicle charging status pipelines. My work integrates Kafka, Postgres, and gold table workflows to streamline data accessibility. With over 5 years of data engineering experience, I bring expertise in developing robust APIs, managing data pipelines, and supporting efficient energy usage and billing systems. My contributions enable seamless front-end application integration and improve data accessibility across systems, aligning with Siemens' mission to deliver innovative technology solutions.
Designed and implemented scalable data pipelines and backend services for vehicle charging, energy usage, and cost data, supporting both real-time and historical workloads. Optimized Kafka-to-gold table pipelines, reducing processing time from 12 minutes to 5 seconds, improving system scalability and efficiency. Migrated streaming workloads from Databricks to a Redis–Postgres hybrid architecture, reducing infrastructure costs and server dependency Developed APIs and data integration pipelines to expose data to front-end and downstream applications, ensuring alignment with data quality standards. Built 15-minute time-bucketed datasets to support accurate billing and reporting, demonstrating strong data modeling and pipeline design skills
Tools used: React.js, Node.js, PySpark, AWS (EC2/RedShift/ S3), Databricks, GitLab, Airflow, JavaScript Implemented serverless web application in AWS, Veracode CI/CD pipeline scan, and terraform driving DR process, Amgen Okta integration process. Migrated IC-HUB website from Flask web framework to serverless architecture with Node.js and React.js based, driving team to be detail-oriented and discussing with business side about website new features and functionality. Developed file quality automation website which provided real time data quality check result, and on-boarded ELMAC countries. Created a runtime monitor UI in Airflow, helping the operation team track the performance of data pipeline and bump up the red flag, took ownership of Airflow 2.0 Upgrade, be the go-to person for Databricks, Airflow in the team. Deployed Flask based website of PDF search engine for POC purposes, deployed airflow docker for China Lighthouse and provided initial pipelines to bring dataset cross S3 and MySQL, be web portal maintainer of Vision Reporting team Designed and implemented data pipeline for the marketing mix dataset, Otezla adoption ladder, reduced the redundant calculations and simplified the pipeline work. Supported manger with Otezla patient analysis, quickly learned the domain knowledge and provided analysis dashboard to business Driven CW team's work to meet all projects milestones and ensure pipelines align with GCOIS architecture, delivered FIT, PulsePoint, GA(US,ANZ,Canada,JAPAN). Analysed TTM data discrepancy cross EDW, GDnA, FIT, EDF and C360, analysed Medical interaction data across EDF, ODS, and C360, found the root cause of data discrepancy and closed the gap cross system.
Tools used: PySpark, AWS (EC2/RedShift/ S3), Databricks, GitLab, Airflow, JavaScript Developed core component, Metric Engine, in iDNA platform, created comprehensive airflow UI to drive iDnA platform, removed operation’s pain points, increased the satisfaction of business clients Managed Airflow disaster recovery of iDNA platform, assisted GDnA Airflow framework integration with Enterprise Data Lake Onboarded Veeva CRM(China) data into GDnA platform, made the project go live ahead of schedule. Implemented new features in MCM for easy historical data tracking Led the efforts to standardize cluster params of iDNA platform, implemented new features to modify params with flexibility and adaptation Supported Customer 360 with cross functional data integration, contributed to continuous improvement of architecture processes Orchestrated automation tools to speed up iDNA CDL process for patient domain
• Built data pipeline for BI dashboard, implement Customer Relationship Management platform • Developed and validated machine learning model for making data-driving decisions • Recognized Twitter Influencers by NLP and data mining model • Developed and validated machine learning model for segmenting customers by Python
• Contributed to operation risk analysis with ML Models, classified client credits, visualized data by Tableau for pre-loan and post-loan risk management, designed workflow for commercial loan approval • Conducted data-driven analysis to strengthen and expand the internal system, prepared data pipeline for internal risk management system, external off-site monitoring system, and national credit reference center system • Led cross-department teams and coordinated with technology service supplier, fulfilled internal settle center system connecting to 20+ financial institutes, reported and presented quarterly risk analysis
• Built, tested and deployed internal reviews feature in company websites • Implemented innovative systems for data collections, storage and retrieval