英汉对照简历 + 重难点词汇&语法解析
整体格式：英文原文 + 中文翻译 逐段对照，重点专业词汇、高阶词汇、长难语法统一标注解释，区分职场通用词、IT专业术语、语法句式。
Resume
简历
Basic Information
基本信息
Name : 布布
姓名 ：
Gender : Male
性别 ：男
Hometown : Xi'an, Shaanxi
籍贯 ：陕西西安
Education : Shaanxi University of Arts and Sciences
毕业院校 ：陕西文理大学
Major : Software Engineering
专业 ：软件工程
Email :
电子邮箱 ：
Phone : 17349410267
联系电话 ：17349410267
Working Years : 5 years
工作年限 ：5年
Applied Position : AI Application Development Engineer
应聘职位：AI应用开发工程师
词汇注解（基础板块）
- hometown /ˈhoʊmtaʊn/ n. 籍贯，故乡（职场简历常用，区别于 native place）
- applied position 应聘岗位；apply for 申请（动词短语）
- software engineering 软件工程（计算机专业固定术语）
Work Experience
工作经历
Sep 2023 -- Present | Digital China Group Co., Ltd.
2023年9月 -- 至今 | 神州数码集团股份有限公司
Aug 2021 -- Aug 2023 | Xi'an Nantian Computer System Co., Ltd.
2021年8月 -- 2023年8月 | 西安南天电脑系统有限公司
词汇注解
- Present /ˈpreznt/ adj. 目前的，现任的（简历时间标配用法）
- Co., Ltd. = Company Limited 有限公司（企业名称标准缩写）
Professional Skills
专业技能
AI Agent & LLM Application
AI智能代理与大模型应用
Proficient in LangChain and LangGraph , capable of designing and orchestrating complex Agent workflows including conditional routing, loop logic and multi-node collaboration.
熟练掌握 LangChain、LangGraph 框架，能够设计并编排复杂智能代理工作流，涵盖条件路由、循环逻辑与多节点协作。
Fully experienced in the full RAG (Retrieval-Augmented Generation) technical stack: document loading, text chunking, embedding, vector retrieval and prompt engineering.
精通完整的RAG（检索增强生成）技术栈：文档加载、文本分块、向量嵌入、向量检索与提示词工程。
Skilled in Chroma vector database and HuggingFace Embeddings (e.g. bge-small-zh) to implement semantic search.
熟练使用 Chroma 向量数据库与 HuggingFace 嵌入模型（如 bge-small-zh）实现语义检索。
Familiar with Codex local deployment and AI-assisted coding.
了解 Codex 本地化部署及AI辅助开发。
Completed end-to-end development of an intelligent customer service system for apparel products based on Python + FastAPI + LangGraph + RAG, supporting both offline and online processing.
独立完成服装商品智能客服系统全流程开发，技术基于 Python + FastAPI + LangGraph + RAG，支持离线与在线双模式处理。
词汇&语法注解
一、核心专业术语（AI/大模型方向）
- AI Agent 智能代理（行业通用译法）
- LLM (Large Language Model) 大语言模型
- orchestrate /ˈɔːrkɪstreɪt/ v. 编排、统筹（高阶词，多用于系统/流程调度）
- workflow /ˈwɜːrkfloʊ/ n. 工作流
- conditional routing 条件路由（技术架构术语）
- multi-node collaboration 多节点协作
- RAG (Retrieval-Augmented Generation) 检索增强生成（大模型核心技术）
- technical stack 技术栈（IT简历高频词）
- text chunking 文本分块
- embedding /ɪmˈbedɪŋ/ n. 向量嵌入（AI领域专属名词）
- vector retrieval 向量检索
- prompt engineering 提示词工程
- vector database 向量数据库
- semantic search 语义搜索
- local deployment 本地化部署
- end-to-end development 端到端开发（指从需求到上线全流程开发）
- apparel products 服饰、服装（正式书面用语，替代普通 clothes）
二、通用职场词汇
- proficient in 精通（程度最强，简历首选；同义：skilled in 熟练；familiar with 了解）
▶ 区分：proficient > skilled > familiar - capable of doing sth 能够做某事（固定搭配，后置定语常用）
三、语法难点
- 长句结构：
Proficient in..., capable of designing... including...
本句为形容词短语并列结构，省略主语(I)，是英文简历经典简写句式，无需完整主谓。
Python Backend Development
Python后端开发
Possess 4 years of Python backend development experience with solid OOP foundation. Master MVC architecture and microservices development using FastAPI / Django REST Framework combined with containerization and service discovery.
拥有4年Python后端开发经验，具备扎实的面向对象编程基础。精通MVC架构、基于FastAPI/Django REST框架的微服务开发，熟悉容器化与服务发现技术。
Experienced in high-concurrency processing with asyncio and Celery asynchronous tasks, transaction management and design patterns, with production-grade system stability design awareness.
熟练使用 asyncio、Celery 异步任务实现高并发处理，掌握事务管理与设计模式，具备生产级系统稳定性设计思维。
Proficient in SQLAlchemy / Django ORM, Swagger/OpenAPI for API documentation, Poetry and Pipenv for dependency management and project packaging.
熟练使用 SQLAlchemy、Django ORM 框架，借助 Swagger/OpenAPI 自动生成接口文档；掌握 Poetry、Pipenv 等依赖管理与项目打包工具。
词汇&语法注解
专业术语
- backend development 后端开发
- OOP (Object-Oriented Programming) 面向对象编程
- MVC architecture MVC架构
- microservices /ˌmaɪkroʊˈsɜːrvɪsɪz/ n. 微服务
- containerization /kənˌteɪnərəˈzeɪʃn/ n. 容器化
- service discovery 服务发现
- high-concurrency processing 高并发处理
- asynchronous tasks 异步任务
- transaction management 事务管理
- design patterns 设计模式
- production-grade 生产级的（形容可线上正式运行的系统）
- API documentation 接口文档
- dependency management 依赖管理
- project packaging 项目打包
词汇&语法
- possess /pəˈzes/ v. 拥有（正式书面词，简历替代 have）
- solid foundation 扎实的基础（固定搭配）
- 句式：
with + 名词作伴随状语（英文简历高频简写，简化从句）
Database & Middleware
数据库与中间件
Skilled in MySQL and Oracle, adept at SQL tuning, index optimization and slow query analysis.
熟练使用 MySQL、Oracle 数据库，精通SQL调优、索引优化与慢查询分析。
Familiar with Redis cache, RabbitMQ message queue and Kettle for data warehouse ETL.
了解 Redis 缓存、RabbitMQ 消息队列，以及基于 Kettle 的数仓ETL开发。
词汇注解
- middleware /ˈmɪdlwer/ n. 中间件（IT专业词）
- be adept at 擅长、精通（同义替换 proficient in）
- SQL tuning SQL调优
- index optimization 索引优化
- slow query analysis 慢查询分析
- message queue 消息队列
- data warehouse 数据仓库
- ETL (Extract-Transform-Load) 数据抽取、转换、加载（数仓核心术语）
Development Tools & O&M
开发工具与运维
Adept at common Linux commands, Git and SVN version control.
熟练使用 Linux 常用命令，掌握 Git、SVN 版本控制工具。
Understand front-end and back-end separation and RPC API design; skilled in troubleshooting with Xshell and Postman.
理解前后端分离架构与RPC接口设计，可使用 Xshell、Postman 完成问题调试。
词汇注解
- O&M (Operation & Maintenance) 运维（行业缩写）
- version control 版本控制
- front-end and back-end separation 前后端分离
- troubleshooting /ˈtrʌblʃuːtɪŋ/ n. 故障排查、问题调试（职场高频词）
Front-end Basics
前端基础
Proficient in Streamlit rapid development framework.
熟练使用 Streamlit 快速开发框架。
Familiar with Vue, Bootstrap, HTML and JavaScript, able to cooperate with front-end teams for joint debugging.
了解 Vue、Bootstrap、HTML、JavaScript 技术栈，可配合前端团队完成联调工作。
词汇注解
- rapid development framework 快速开发框架
- joint debugging 联合调试、联调（开发协作专用词）
Project Experience
项目经验
1. Intelligent Secretary System based on LangGraph (Agent + MCP + Long-term Memory)
1. 基于LangGraph的智能小秘书系统（智能代理+MCP协议+长期记忆）
Project Background
项目背景
In office scenarios, users need cross-application operations such as train ticket inquiry, data chart generation and web information retrieval, along with persistent user preference memory. Traditional LLMs lack active tool calling and cross-session memory capabilities. This project built an intelligent Agent system with tool invocation, manual intervention and long-term memory.
在办公场景中，用户需要跨应用操作（火车票查询、数据图表生成、网络信息获取等），同时要求系统记忆个人偏好。传统大模型无法主动调用工具，也不具备跨会话记忆能力。本项目搭建了一套集工具调用、人工干预、长期记忆于一体的智能代理系统。
Tech Stack
技术栈
LangGraph, MCP (Model Context Protocol) & FastMCP, PostgreSQL, LangChain, Qwen (Alibaba DashScope), aiohttp, Matplotlib, Pandas
LangGraph、MCP（模型上下文协议）、FastMCP、PostgreSQL、LangChain、通义千问（阿里云百炼）、aiohttp、Matplotlib、Pandas
Core Capabilities
核心功能
- Tool Calling: Encapsulated train ticket query, chart generation and web data acquisition as independent MCP services for Agent on-demand invocation.
- 工具调用：将火车票查询、图表生成、网络数据获取封装为独立MCP服务，供智能代理按需调用。
- Long-term Memory: Store user information across conversations for continuous reference.
- 长期记忆：跨会话保存用户信息，支持历史内容复用。
- Manual Intervention: Add confirmation nodes for high-risk operations to avoid misoperation.
- 人工干预：为高危操作增加确认节点，规避误操作风险。
- State Persistence: Store long-term memory in PostgreSQL and short-term session state via MemorySaver.
- 状态持久化：使用PostgreSQL存储长期记忆，通过MemorySaver保存会话短期状态。
Technical Difficulties & Solutions
技术难点与解决方案
- Low tool selection accuracy with increasing tools: Standardized all tools via MCP, and adopted LangGraph conditional routing to distinguish operations requiring manual review.
- 工具增多后选择准确率下降：通过MCP统一工具标准，结合LangGraph条件路由区分需人工审核的操作。
- Cross-session memory loss: Used AsyncPostgresStore with namespace isolation to store user data, and automatically update memory by parsing user input.
- 跨会话记忆丢失：采用带命名空间隔离的AsyncPostgresStore存储用户数据，并解析用户输入自动更新记忆。
- Blocked workflow during manual confirmation: Adopted asyncio thread pool to process blocking input without disrupting the event loop.
- 人工确认导致工作流阻塞：使用asyncio线程池处理阻塞式输入，不影响事件循环正常运行。
- MCP tool adaptation: Integrated multiple MCP Servers via langchain-mcp-adapters and converted services into standard LangChain tools.
- MCP工具适配：通过langchain-mcp-adapters对接多组MCP服务端，将服务转为标准LangChain工具。
Project Achievements
项目成果
New MCP tools can be integrated by adding individual Python files without modifying core logic. Full-asynchronous architecture reduces response time for multi-tool requests by over 60%. The system features high scalability and can be migrated to customer service, education, finance and other industries.
新增MCP工具仅需编写独立Python文件，无需改动核心逻辑。全异步架构将多工具请求响应速度提升60%以上。系统扩展性强，可快速迁移至客服、教育、金融等多个行业。
重点词汇&语法
- scenario /səˈnærioʊ/ n. 场景、业务场景（商务/技术文档高频）
- cross-application 跨应用
- persistent /pərˈsɪstənt/ adj. 持久的
- preference /ˈprefrəns/ n. 偏好
- capability /ˌkeɪpəˈbɪləti/ n. 能力、功能
- encapsulate /ɪnˈkæpsjuleɪt/ v. 封装（编程核心词）
- on-demand invocation 按需调用
- state persistence 状态持久化
- namespace isolation 命名空间隔离
- parse /pɑːrs/ v. 解析（代码/文本处理）
- thread pool 线程池
- event loop 事件循环
- adaptation /ˌædæpˈteɪʃn/ n. 适配
- scalability /ˌskeɪləˈbɪləti/ n. 可扩展性（系统评价核心词）
- migrate /maɪˈɡreɪt/ v. 迁移
2. Intelligent Customer Service Q&A System based on LangChain (RAG)
2. 基于LangChain的智能客服问答系统（RAG架构）
Project Background
项目背景
Developed an apparel e-commerce intelligent customer service system empowered by private knowledge base (size recommendation, maintenance guidance, matching suggestions, etc.), solving LLM hallucination and inability to access offline private data via RAG architecture.
搭建电商服装类智能客服系统，依托私有知识库（尺码推荐、养护指南、搭配建议等），通过RAG架构解决大模型幻觉、无法读取离线私有数据的问题。
Tech Stack
技术栈
LangChain, LangChain-Community, Chroma, DashScope (text-embedding-v4, qwen3-max), Streamlit
LangChain、LangChain-Community、Chroma、阿里云百炼（向量模型text-embedding-v4、大模型qwen3-max）、Streamlit
Technical Difficulties & Solutions
技术难点与解决方案
- Duplicate data storage: Adopted MD5 fingerprint deduplication for knowledge documents.
- 数据重复存储：对知识库文档采用MD5指纹去重。
- Information loss after text splitting: Set chunk overlap and optimized delimiter priority.
- 文本分割后信息丢失：设置文本重叠区，并优化分隔符优先级。
- LLM hallucination: Restricted the model to prioritize retrieved knowledge and configured fallback replies for irrelevant questions.
- 大模型幻觉问题：限制模型优先引用检索内容，并针对无关问题配置兜底回复。
- Lost multi-turn dialogue context: Implemented persistent chat history and user isolation via session ID.
- 多轮对话上下文丢失：实现聊天记录持久化，通过会话ID隔离不同用户。
Project Achievements
项目成果
The whole process of document parsing, embedding and warehousing completes within 10 seconds with incremental update supported. The system greatly improves Q&A accuracy and cuts API costs via local vector database and deduplication. The architecture is reusable for finance, medical, legal and other industries.
文档解析、向量化、入库全流程耗时低于10秒，支持增量更新。依托本地向量库与去重机制，大幅提升问答准确率并降低接口调用成本。架构可复用于金融、医疗、法律等行业。
重点词汇
- e-commerce 电子商务
- knowledge base 知识库
- hallucination /həˌluːsɪˈneɪʃn/ n. （大模型）幻觉（AI专属术语）
- fingerprint deduplication 指纹去重
- chunk overlap 文本重叠区
- delimiter /dɪˈlɪmɪtər/ n. 分隔符
- prioritize /praɪˈɔːrətaɪz/ v. 优先处理、优先使用
- fallback reply 兜底回复
- multi-turn dialogue 多轮对话
- session ID 会话ID
- incremental update 增量更新
- reusable /ˌriːˈjuːzəbl/ adj. 可复用的
3. Data Migration Module for Bohai Bank New Core System
3. 渤海银行新核心系统数据迁移模块
Project Background
项目背景
Led the full-process data migration of loan business from legacy system to new core system, including business logic analysis, data mapping, data cleansing, conversion and verification to ensure data integrity and business continuity.
主导贷款业务从旧系统到新核心系统的全流程数据迁移，包含业务逻辑分析、数据映射、数据清洗、转换与校验，保障数据完整性与业务连续性。
Tech Stack
技术栈
Python, DB2, Oracle, Kettle, Excel, Stored Procedure, Maven, Git
Python、DB2、Oracle、Kettle、Excel、存储过程、Maven、Git
Responsibilities
工作内容
Designed migration schemes and automated ETL processes with Kettle. Built multi-dimensional data verification rules to fix data defects. Optimized SQL and stored procedures, completed 4 rounds of full data testing, and delivered migration specifications and emergency plans.
基于Kettle设计迁移方案与自动化ETL流程，搭建多维度数据校验规则以修复数据异常。优化SQL与存储过程，完成4轮全量数据测试，输出迁移规范与应急预案。
重点词汇
- data migration 数据迁移
- legacy system 旧系统、遗留系统（IT通用词）
- data mapping 数据映射
- data cleansing 数据清洗
- data integrity 数据完整性
- business continuity 业务连续性
- stored procedure 存储过程（数据库术语）
- data defect 数据缺陷、数据异常
- emergency plan 应急预案
4. Loan Core Module for Taizhou Bank New Core System
4. 台州银行新核心系统贷款核心模块
Project Background
项目背景
Built an integrated credit business system covering contract creation, loan disbursement, repayment, post-loan management and data modification, to streamline business processes and reduce operational risks.
搭建一体化信贷业务系统，覆盖合同创建、放款、还款、贷后管理、信息变更等功能，简化业务流程、降低操作风险。
Tech Stack
技术栈
SpringBoot, Sonic Batch, TDSQL, RabbitMQ, Maven, Git
SpringBoot、Sonic批处理框架、TDSQL、RabbitMQ、Maven、Git
Responsibilities
工作内容
Developed accounting and non-accounting functions for loan business. Implemented RabbitMQ message queue services. Maintained batch tasks including daily cutover, automatic deduction and batch disbursement.
开发贷款业务账务与非账务功能，实现RabbitMQ消息队列服务。负责日切、自动扣款、批量放款等批处理任务维护。
重点词汇
- credit business 信贷业务
- loan disbursement 贷款放款
- post-loan management 贷后管理
- streamline /ˈstriːmlaɪn/ v. 精简、优化（流程）
- operational risks 操作风险
- batch tasks 批处理任务
- daily cutover 日切（银行系统专属术语）
- automatic deduction 自动扣款
5. Deposit Module for Singapore Gulf Bank Core System
5. 新加坡海湾银行核心系统存款模块
Project Background
项目背景
Developed core banking systems covering customer account management, transaction processing, interest settlement, remittance and approval workflows.
开发银行核心系统，涵盖客户账户管理、交易处理、计息结算、汇款、审批流程等功能。
Tech Stack
技术栈
SpringBoot, Sonic Batch, Redis, Vue, Maven, Git
SpringBoot、Sonic批处理框架、Redis、Vue、Maven、Git
Responsibilities
工作内容
Completed front-end and back-end development for personal & corporate account management. Optimized SQL statements and adopted Redis cache to improve system response speed. Conducted system testing and compiled user operation manuals for overseas banking business.
完成个人账户、对公账户的前后端开发；优化SQL语句并引入Redis缓存提升系统响应速度；开展系统测试，并编写海外银行业务用户操作手册。
重点词汇
- deposit module 存款模块
- core banking system 银行核心系统
- interest settlement 计息结算
- remittance /rɪˈmɪtns/ n. 汇款
- approval workflow 审批流程
- corporate account 对公账户
- compile /kəmˈpaɪl/ v. 编写、编撰（文档、手册）
- operation manual 操作手册
Hobbies & Interests
特长爱好
Explore cutting-edge technologies; Host talk shows and crosstalk; Enjoy sports and music
钻研前沿技术；主持脱口秀、相声表演；热爱运动与音乐
词汇注解
- cutting-edge technologies 前沿技术（高阶搭配，替代 new technologies）
- crosstalk /ˈkrɔːstɔːk/ n. 相声（中式文化专属英文译法）
Self-evaluation
自我评价
I have solid professional capabilities, strong learning ability and communication skills. I can quickly adapt to new working environments with excellent pressure resistance. I work rigorously and responsibly, and possess great team spirit.
本人专业能力扎实，学习与沟通能力良好；可快速适应新环境，抗压能力强；工作严谨负责，具备优秀的团队协作精神。
词汇&语法注解
- capability /ˌkeɪpəˈbɪləti/ n. 综合能力（正式书面语）
- adapt to 适应（固定短语）
- pressure resistance 抗压能力（职场简历标配）
- rigorously /ˈrɪɡərəsli/ adv. 严谨地、认真地
- team spirit 团队精神（固定搭配）
- 句式：全篇使用简单并列句，句式简洁正式，符合欧美简历书写习惯。
Resume
Basic Information
Name : Guo Wei
Gender : Male
Hometown : Xi'an, Shaanxi
Education : Shaanxi University of Arts and Sciences
Major : Software Engineering
Email : 872059781@qq.com
Phone : 17349410267
Working Years : 5 years
Applied Position: AI Application Development Engineer
Work Experience
Sep 2023 -- Present | Digital China Group Co., Ltd.
Aug 2021 -- Aug 2023 | Xi'an Nantian Computer System Co., Ltd.
Professional Skills
AI Agent & LLM Application
Proficient in LangChain and LangGraph , capable of designing and orchestrating complex Agent workflows including conditional routing, loop logic and multi-node collaboration.
Fully experienced in the full RAG (Retrieval-Augmented Generation) technical stack: document loading, text chunking, embedding, vector retrieval and prompt engineering.
Skilled in Chroma vector database and HuggingFace Embeddings (e.g. bge-small-zh) to implement semantic search.
Familiar with Codex local deployment and AI-assisted coding.
Completed end-to-end development of an intelligent customer service system for apparel products based on Python + FastAPI + LangGraph + RAG, supporting both offline and online processing.
Python Backend Development
Possess 4 years of Python backend development experience with solid OOP foundation. Master MVC architecture and microservices development using FastAPI / Django REST Framework combined with containerization and service discovery.
Experienced in high-concurrency processing with asyncio and Celery asynchronous tasks, transaction management and design patterns, with production-grade system stability design awareness.
Proficient in SQLAlchemy / Django ORM, Swagger/OpenAPI for API documentation, Poetry and Pipenv for dependency management and project packaging.
Database & Middleware
Skilled in MySQL and Oracle, adept at SQL tuning, index optimization and slow query analysis.
Familiar with Redis cache, RabbitMQ message queue and Kettle for data warehouse ETL.
Development Tools & O&M
Adept at common Linux commands, Git and SVN version control.
Understand front-end and back-end separation and RPC API design; skilled in troubleshooting with Xshell and Postman.
Front-end Basics
Proficient in Streamlit rapid development framework.
Familiar with Vue, Bootstrap, HTML and JavaScript, able to cooperate with front-end teams for joint debugging.
Project Experience
1. Intelligent Secretary System based on LangGraph (Agent + MCP + Long-term Memory)
Project Background
In office scenarios, users need cross-application operations such as train ticket inquiry, data chart generation and web information retrieval, along with persistent user preference memory. Traditional LLMs lack active tool calling and cross-session memory capabilities. This project built an intelligent Agent system with tool invocation, manual intervention and long-term memory.
Tech Stack
LangGraph, MCP (Model Context Protocol) & FastMCP, PostgreSQL, LangChain, Qwen (Alibaba DashScope), aiohttp, Matplotlib, Pandas
Core Capabilities
- Tool Calling: Encapsulated train ticket query, chart generation and web data acquisition as independent MCP services for Agent on-demand invocation.
- Long-term Memory: Store user information across conversations for continuous reference.
- Manual Intervention: Add confirmation nodes for high-risk operations to avoid misoperation.
- State Persistence: Store long-term memory in PostgreSQL and short-term session state via MemorySaver.
Technical Difficulties & Solutions
- Low tool selection accuracy with increasing tools: Standardized all tools via MCP, and adopted LangGraph conditional routing to distinguish operations requiring manual review.
- Cross-session memory loss: Used AsyncPostgresStore with namespace isolation to store user data, and automatically update memory by parsing user input.
- Blocked workflow during manual confirmation: Adopted asyncio thread pool to process blocking input without disrupting the event loop.
- MCP tool adaptation: Integrated multiple MCP Servers via langchain-mcp-adapters and converted services into standard LangChain tools.
Project Achievements
New MCP tools can be integrated by adding individual Python files without modifying core logic. Full-asynchronous architecture reduces response time for multi-tool requests by over 60%. The system features high scalability and can be migrated to customer service, education, finance and other industries.
2. Intelligent Customer Service Q&A System based on LangChain (RAG)
Project Background
Developed an apparel e-commerce intelligent customer service system empowered by private knowledge base (size recommendation, maintenance guidance, matching suggestions, etc.), solving LLM hallucination and inability to access offline private data via RAG architecture.
Tech Stack
LangChain, LangChain-Community, Chroma, DashScope (text-embedding-v4, qwen3-max), Streamlit
Technical Difficulties & Solutions
- Duplicate data storage: Adopted MD5 fingerprint deduplication for knowledge documents.
- Information loss after text splitting: Set chunk overlap and optimized delimiter priority.
- LLM hallucination: Restricted the model to prioritize retrieved knowledge and configured fallback replies for irrelevant questions.
- Lost multi-turn dialogue context: Implemented persistent chat history and user isolation via session ID.
Project Achievements
The whole process of document parsing, embedding and warehousing completes within 10 seconds with incremental update supported. The system greatly improves Q&A accuracy and cuts API costs via local vector database and deduplication. The architecture is reusable for finance, medical, legal and other industries.
3. Data Migration Module for Bohai Bank New Core System
Project Background
Led the full-process data migration of loan business from legacy system to new core system, including business logic analysis, data mapping, data cleansing, conversion and verification to ensure data integrity and business continuity.
Tech Stack
Python, DB2, Oracle, Kettle, Excel, Stored Procedure, Maven, Git
Responsibilities
Designed migration schemes and automated ETL processes with Kettle. Built multi-dimensional data verification rules to fix data defects. Optimized SQL and stored procedures, completed 4 rounds of full data testing, and delivered migration specifications and emergency plans.
4. Loan Core Module for Taizhou Bank New Core System
Project Background
Built an integrated credit business system covering contract creation, loan disbursement, repayment, post-loan management and data modification, to streamline business processes and reduce operational risks.
Tech Stack
SpringBoot, Sonic Batch, TDSQL, RabbitMQ, Maven, Git
Responsibilities
Developed accounting and non-accounting functions for loan business. Implemented RabbitMQ message queue services. Maintained batch tasks including daily cutover, automatic deduction and batch disbursement.
5. Deposit Module for Singapore Gulf Bank Core System
Project Background
Developed core banking systems covering customer account management, transaction processing, interest settlement, remittance and approval workflows.
Tech Stack
SpringBoot, Sonic Batch, Redis, Vue, Maven, Git
Responsibilities
Completed front-end and back-end development for personal & corporate account management. Optimized SQL statements and adopted Redis cache to improve system response speed. Conducted system testing and compiled user operation manuals for overseas banking business.
Hobbies & Interests
Explore cutting-edge technologies; Host talk shows and crosstalk; Enjoy sports and music
Self-evaluation
I have solid professional capabilities, strong learning ability and communication skills. I can quickly adapt to new working environments with excellent pressure resistance. I work rigorously and responsibly, and possess great team spirit.