Big Data Engineer_BST at Bosch Group

Suzhou, Jiangsu, China -

Full Time

Start Date

Immediate

Expiry Date

09 Jun, 26

Salary

0.0

Posted On

11 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PySpark, Spark SQL, ETL, ELT, Data Modeling, Data Quality, Tableau, SQL, Data Warehouse, Spark Architecture, Distributed Data Processing, Data Engineering, Big Data Development

Industry

Software Development

Description

Company Description Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology - with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch. Job Description Role Overview We are looking for a Big Data Engineer to join our data team and lead the development of scalable data pipelines and analytics workflows. The role focuses on processing large-scale test and experimental data using PySpark, building reliable data models, and enabling downstream analytics and visualization in Tableau. You will work closely with sensor experts, test engineers, and infrastructure engineers to transform raw testing data into high-quality datasets that support operational monitoring, performance analysis, and decision making. Key Responsibilities Data Pipeline Development Design, build, and maintain scalable data pipelines using PySpark and Spark SQL Process and transform large volumes of structured and semi-structured test data Implement efficient ETL / ELT workflows for data ingestion, cleaning, transformation, and aggregation Optimize Spark jobs for performance, reliability, and cost efficiency Data Modeling & Data Quality Design data models and curated datasets optimized for analytics and reporting Implement data validation, monitoring, and quality checks Analytics Enablement Prepare analysis-ready datasets for downstream analytics Collaborate with analysts to support Tableau dashboards and reports Optimize data structures to improve query performance for visualization tools Collaboration Work in a global team closely with data engineers, test engineers, and product stakeholders Translate analytical requirements into scalable data solutions Review code and contribute to best practices in data engineering Qualifications 3+ years experience in data engineering or big data development Strong experience with Python and PySpark Deep understanding of Spark architecture and distributed data processing Experience designing ETL pipelines and data models Familiarity with SQL and data warehouse concepts Experience supporting BI tools (Tableau preferred) 博世集团网上招聘数据处理同意声明个人信息及目的：你的如下个人信息将被处理并用于招聘：姓名、电子邮箱、电话号码、所在地区、经验和教育信息、简历等应聘者自主提交的附件信息、证件号、职位、部门、计划入职日期、劳动合同持续时间、工作地点、月薪、年薪、浮动奖金比例、餐贴、车贴。博世将依据相关的数据保护法规，在全球范围内对您的数据进行保密。招聘流程中，您的个人信息只有在必须时才会被提供给博世以外的第三方机构(如代理机构)。博世精心挑选了第三方机构并与之签订了保密合同。博世采取了种种保密措施以保护您的数据，确保其不会遭受操纵、丢失、破坏、未经授权的访问或泄漏。我们的安全措施将随着新技术的应用而不断升级。您的本地浏览器和我们的人才招募系统之间的数据传输通过https加密。特定职位申请：申请博世集团的职位就意味着您将自己的个人信息提供给博世集团旗下相应的法律实体（具体请见博世集团法人清单）。请注意，若有任何文件或资料变更，请在所有申请书里一并变更。人力资源部门的员工可能与您联系，询问您是否同意将您的求职申请转向博世集团内部另一合适职位。如果您申请了特定职位，那么只有在您同意的情况下，博世才会将您的求职申请转向另一职位。您可以随时撤回求职申请，博世将根据相关法律删除您的个人信息。与您的申请相关的所有资料将被保留至24个月。您的权利：若您希望停止向博世提供您的个人信息，可联系我们或通过博世网上招聘数据隐私声明中的BKMS系统提出申请。您可以在SmartRecruiters平台查阅和修改您的简历。您对您的个人信息的处理享有知情权、决定权，您有权限制或者拒绝我们对您的个人信息进行处理。您还可以更正、补充您的个人信息。跨境传输：为了实现前述目的，我们所收集的您的个人信息可能会在多个国家或地区间进行跨境转移，例如德国、新加坡。德国：罗伯特-博世有限公司（德国格宁根市罗伯特博世广场1号（Robert-Bosch-Platz 1, Gerlingen-Schillerhohe, Germany，邮编：70839），用于集团统一招聘管理（博世中国与数据接收方均基于该目的开展数据出境活动；英国：SmartRecruiters（英国伯克郡温莎亚瑟路圣斯蒂芬大厦邮政区码: SL4 1RU），用于招聘、后期交流联系及分享职位/活动信息、潜在人才社区邀请。您可通过上述联系方式依法向境外接收方行使您在个人信息处理活动中的权利。存储您的个人信息：我们将仅在为实现目的必要的范围内以及法律法规要求的时间内保留您的个人信息。雇佣：被雇佣后，您提供的数据将被从当前招聘系统转移至职位所属公司的人力资源管理系统。联系方式：请通过访问博世中国官方主页：博世在中国＞加入博世＞工作机会，在网上招聘数据隐私声明中“9.联系方式” 博世集团法人清单：请通过访问博世中国官方主页：博世在中国＞加入博世＞工作机会，在网上招聘数据隐私声明中获取现阶段使用网上招聘系统的博世集团法人（第五页） Legal Entity: Bosch (China) Investment Ltd. Suzhou Branch

Responsibilities

The role involves leading the development of scalable data pipelines and analytics workflows, focusing on processing large-scale test and experimental data using PySpark and building reliable data models. Key tasks include designing, building, and maintaining ETL/ELT workflows, optimizing Spark jobs, and preparing analysis-ready datasets for downstream analytics and Tableau visualization.