思特沃克
Senior Data Engineer
Senior Data Engineer
发布于 5 个月前普通员工/个人贡献者
Quito, Ecuador
高级经验
全职员工
远程工作
学历未注明
软件工程
分布式系统
数据可视化
数据安全
数据建模
数据治理
数据管道
Gen/Ai
LLMs
TDD
AI 估算 · 45k–70k
高级数据工程师岗位，需掌握前沿技术栈（如AI/LLMs、数据网格），技能门槛高，市场竞争力强，薪资水平位于行业前列。
职位详情
关于这个职位
作为思特沃克的高级数据工程师，你将负责构建、维护和测试数据应用的基础设施与软件架构
你将使用最新的数据工具（如数据网格）开发复杂的数据处理管道，并与数据科学家合作，将模型规模化落地
你的工作将直接解决客户最具挑战性的问题，并确保数据治理、安全与质量
最低要求
技术技能：
具备 Databricks 平台使用经验
具备 Gen/AI 和 LLMs 经验
对数据处理充满热情
能够在分布式系统中构建和操作数据管道，并维护数据存储
具备数据建模和现代数据工程工具与平台的实践经验
具备使用首选编程语言编写高质量、整洁代码的经验
具备在生产环境中使用任何分布式存储和处理平台构建并部署大规模数据管道和数据中心应用的经验
具备数据可视化技术经验，并能根据受众传达见解
具备数据驱动方法经验，并能应用数据安全和隐私策略解决业务问题
具备不同类型数据库（如：SQL、NoSQL、数据湖、数据模式等）的经验
专业技能：
具备高级英语水平
理解利益相关者管理的重要性，能够在项目过程中轻松地与客户和其他关键利益相关者联络，确保获得支持并赢得信任
在模糊情况下具有韧性，能够调整角色以从多个角度应对挑战
不回避风险或冲突，而是勇于承担并巧妙地管理它们
渴望指导、激励他人，并希望影响队友采取积极行动并对工作负责
乐于影响他人，始终倡导技术卓越，同时在需要时乐于接受改变
工作职责
开发和运营现代数据架构方法，以满足关键业务目标并提供端到端的数据解决方案
开发复杂的数据处理管道，解决客户最具挑战性的问题
与数据科学家合作，设计其模型的可扩展实现
使用 TDD 编写整洁、迭代的代码，并利用各种持续交付实践来部署、支持和操作数据管道
从众多可用选项中使用不同的分布式存储和计算技术
通过从多种建模技术中选择来开发数据模型，并使用适当的技术栈实现所选数据模型
与团队在数据治理、数据安全和数据隐私领域进行协作
将数据质量融入日常工作
AI 洞察
优缺点分析
优点
- Work with cutting-edge technologies like Gen/AI, LLMs, and data mesh at a leading global technology consultancy.
- Opportunity to solve diverse and challenging business problems for high-profile clients, building a strong portfolio.
- Strong focus on learning and development with interactive tools, programs, and a supportive peer culture that empowers career growth.
- High expectations for technical excellence and the ability to deliver solutions in ambiguous or complex client environments.
- Need to balance deep technical work with client liaison, stakeholder management, and mentoring responsibilities.
- Keeping pace with the rapidly evolving landscape of big data tools, frameworks, and AI technologies.
- This role is ideal for experienced data engineers who thrive on technical challenges, enjoy client interaction and mentoring, and want to work at the forefront of data and AI technology within a collaborative, global environment.
缺点 / 挑战
暂无明显挑战项
角色解读
- Technical leadership path: Progress to architect roles, specializing in cutting-edge areas like data mesh or AI/ML infrastructure.
- Consulting and client-facing path: Develop deeper expertise in solving complex business problems across different industries, potentially moving into solution architecture or technical strategy roles.
- Design and build modern data architectures and end-to-end data solutions to meet business objectives.
- Develop and operate intricate data processing pipelines to solve complex client problems using the latest big data tools.
- Collaborate with data scientists to implement scalable models and ensure data governance, security, and quality standards.
- Expertise in modern data engineering platforms like Databricks, and experience with Gen/AI and LLMs.
- Strong skills in building and operating data pipelines within distributed systems, and proficiency in data modeling and various database technologies.
- Advanced English communication and stakeholder management skills, with the ability to coach others and advocate for technical excellence.
申请策略
- Research Thoughtworks' projects and public tech insights to understand their approach to technology and problem-solving.
- Be prepared to discuss not just technical skills, but also your philosophy on collaboration, continuous learning, and advocating for technical excellence.
- Quantify your experience with specific platforms (Databricks) and technologies (Gen/AI, LLMs), and detail your role in building and deploying large-scale data pipelines.
- Showcase projects where you solved complex data problems, emphasizing your approach to data modeling, governance, security, and quality.
- Highlight instances of stakeholder management, client collaboration, and any experience mentoring or coaching other team members.
- If less familiar, deepen practical knowledge of the Databricks platform and explore hands-on projects involving modern data architecture patterns like data mesh.
- Brush up on advanced data modeling techniques and ensure fluency in discussing data security, privacy strategies, and governance frameworks.
- Practice articulating technical concepts and project experiences clearly in English, focusing on storytelling for both technical and non-technical audiences.
面试指南
- Use the STAR method (Situation, Task, Action, Result) to structure your answers, providing concrete examples.
- Focus on the 'why' behind your technical choices, demonstrating your problem-solving and strategic thinking.
- Emphasize not just the technical outcome, but also the collaborative process, lessons learned, and business impact.
- Walk us through your experience designing and implementing a large-scale data pipeline. What challenges did you face and how did you overcome them?
- How have you applied data governance and security principles in your past projects?
- Describe a time you had to collaborate with data scientists or other non-engineering stakeholders to deliver a data solution.
- Tell us about your experience with the Databricks platform and/or working with Gen/AI/LLM technologies.
- How do you approach mentoring junior team members or advocating for technical best practices within a team?
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