思特沃克
Lead Data Solution Architect
Lead Data Solution Architect
发布于 5 个月前基层主管/组长
Singapore, Singapore
专家级经验
全职员工
远程工作
学历未注明
软件工程
云环境
人工智能
分布式系统
数据建模
数据架构
数据治理
机器学习
AI 估算 · 80k–120k
作为顶尖科技咨询公司的首席架构师，需要精通前沿数据技术和具备领导力，新加坡市场对此类专家需求旺盛，薪资竞争力强。
职位详情
关于这个职位
作为首席数据解决方案架构师，您将负责设计和领导现代数据架构方案，以满足业务目标并提供端到端的数据解决方案
您将在项目中担任技术领导角色，指导团队进行架构决策，并与客户及内部团队协作，确保数据战略的落地实施
最低要求
具备定义和实施不同类型数据架构的经验，能够分析权衡并为不同类型的数据架构定义技术栈
有基于大数据、人工智能及相关技术设计应用系统架构的经验
有构建、维护和调优数据平台的经验，以及数据仓库设计、数据建模、数据监控和维护的经验
熟悉常见的设计模式、应用框架和基础/理论知识（如：分布式系统、数据密集型应用等）
精通常见的开源分布式计算/存储技术，包括但不限于YARN、Impala、Spark、MapReduce、Kafka和Flink，并具备实际项目架构经验
具备良好的业务理解、沟通和协作能力，以及强大的学习和总结能力
有定义、开发和启用数据驱动技术、高级分析、ML/AI和数据挖掘应用在企业中的经验
这包括LLMs、VLMs、向量和图数据库和/或代理架构等技术
有开发实时和低延迟数据流解决方案的经验
有将机器学习模型投入生产以及应用相关技术、工具和流程的经验
对数据基础设施和运维充满热情，具备在云环境中工作的专业知识
具备影响他人的经验，始终倡导技术卓越，同时在需要时乐于改变
能够通过帮助将业务需求转化为软件需求来连接产品和技术的桥梁
具备制定和执行以业务价值为重点的技术愿景的能力
能够在模糊的情况下保持韧性，并能从多个角度应对挑战
工作职责
在支持下，导航数据项目的架构问题，使交付团队能够在时间和预算内按照既定标准交付
提供面向客户的技术领导和指导，就数据架构、工程和分析相关主题向客户提供建议，帮助客户实现其数据战略
与企业架构组的客户对应方互动，以交付、分享、协调和签署关键的架构决策、权衡和工作方式
向工程师和业务利益相关者传达数据架构的高层和底层技术细节
理解并能够在分析和运营架构的交汇处进行协作
在支持下领导数据治理、数据安全和数据隐私的技术设计，以满足合规要求
在支持下领导纳入数据质量框架和流程，以解决和满足战略和验收标准中规定的要求
与销售和售前团队合作，澄清需求并设计可行的解决方案
作为导师，通过技术知识和激励团队共同提供非凡影响的能力，为经验较少的同事提供指导
AI 洞察
优缺点分析
优点
- Work with cutting-edge technologies like AI, ML, and real-time data streaming at a leading global technology consultancy.
- Opportunity to mentor teams and influence technical direction, building strong leadership and architectural skills.
- Exposure to diverse client projects across industries, providing broad experience and professional network growth.
- High responsibility for architectural decisions that impact project timelines, budgets, and client outcomes.
- Need to constantly learn and adapt to rapidly evolving data technologies and client requirements.
- Balancing technical leadership with client-facing responsibilities and team mentorship can be demanding.
- This role is ideal for experienced data architects who enjoy technical leadership, client interaction, and mentoring teams in a dynamic consulting environment.
缺点 / 挑战
暂无明显挑战项
角色解读
- Can progress to senior leadership roles such as Head of Data Architecture or Chief Data Officer, shaping enterprise-wide data strategy.
- Opportunities to specialize further in emerging areas like AI/ML, real-time analytics, or data governance, becoming a subject matter expert.
- Lead the design and implementation of modern data architectures to meet business objectives and deliver end-to-end data solutions.
- Provide technical leadership and guidance to both internal teams and clients on data architecture, engineering, and analytics topics.
- Collaborate with enterprise architecture groups and stakeholders to align on and sign off key architectural decisions and trade-offs.
- Expertise in designing and implementing various data architectures, including big data, AI, and data warehousing solutions.
- Proficiency in open-source distributed technologies like Spark, Kafka, Flink, and experience with cloud environments.
- Strong skills in data modeling, data governance, security, and the ability to productionize machine learning models.
申请策略
- Research Thoughtworks' projects, culture, and their approach to technology consulting to align your application with their values.
- Prepare to discuss not just technical skills but also your experience in collaboration, mentorship, and driving business value through technology.
- Emphasize specific projects where you designed and led the implementation of complex data architectures, detailing the technologies used and business impact.
- Highlight experience in client-facing roles, technical leadership, and mentoring junior team members or guiding architectural decisions.
- Showcase expertise in key technologies mentioned (Spark, Kafka, cloud platforms, ML/AI) and experience with data governance and security.
- Deepen knowledge of the latest AI/ML trends, large language models (LLMs), and real-time data streaming frameworks if not already proficient.
- Practice articulating complex architectural trade-offs and decisions to both technical and non-technical stakeholders.
- Brush up on data governance frameworks, compliance requirements, and best practices for data security and privacy.
面试指南
- Use the STAR method (Situation, Task, Action, Result) to structure your answers, focusing on specific examples and quantifiable outcomes.
- Balance technical depth with business impact. Explain not just what you did, but why it mattered to the project or client.
- Demonstrate a collaborative mindset. Highlight how you worked with teams and stakeholders, and how you handled differing viewpoints.
- Describe a complex data architecture you designed. What were the key challenges, trade-offs, and how did you ensure it met business goals?
- How do you approach mentoring a team on architectural decisions and ensuring technical excellence while being open to change?
- Explain your experience with productionizing machine learning models and ensuring they meet performance and compliance requirements.
- Tell us about a time you had to collaborate with client stakeholders to align on and sign off critical architectural decisions.
- How do you stay updated with evolving data technologies, and how would you apply that knowledge in this role?
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