数据工程师负责人 - AWS
Lead Data Engineer - AWS
Tiger Analytics 是一家快速发展的高级分析咨询公司。我们的顾问在数据科学、机器学习和人工智能领域拥有深厚的专业知识。我们是多家财富 500 强公司的可信赖分析合作伙伴，帮助他们从数据中创造商业价值。我们的商业价值和领导力得到了包括 Forrester 和 Gartner 在内的多家市场研究机构的认可。随着我们继续打造全球最佳的分析咨询团队，正在寻找顶尖人才加入。
Tiger Analytics 正在寻找一位经验丰富的高级数据工程师加入我们的团队，专注于在 AWS 生态系统中构建可扩展的生成式 AI 架构。您将负责构建为我们的财富 500 强合作伙伴的 LLM 和自主代理提供支持的数据基础架构。
关键职责：
- GenAI 基础设施：使用 Amazon Bedrock 和 Amazon SageMaker 构建、部署和扩展生成式 AI 应用程序的数据管道。
- 向量基础：使用 Amazon OpenSearch Serverless 或专用向量引擎实现并优化向量搜索功能，用于 RAG（检索增强生成）。
- 无服务器数据工程：使用 AWS Lambda、AWS Glue 和 Amazon Kinesis 构建高度可扩展的事件驱动 ETL 数据管道。
- 现代数据栈：利用 Amazon S3、AWS Lake Formation 和 Amazon Redshift 管理大规模数据湖仓。
- LLM 运维：集成 AWS Step Functions 和 SageMaker Pipelines，自动化基础模型的微调和部署。
要求
- 经验：8-12 年数据工程经验，重点在 AWS 云技术栈。
- AWS 专业知识：对 Glue、Athena、EMR 和 Redshift 有深入的实际操作经验。
- AI/ML 工具：熟练使用与 AWS 服务集成的 LangChain 或 LlamaIndex，处理非结构化数据（文本、图像、PDF）。
- DevOps 与 IAC：有使用 AWS CDK 或 Terraform 部署基础设施的经验。
- 核心技能：具备高级 SQL、Python 和 PySpark 技能，适用于 AWS 上的分布式处理。
福利
此职位为在快速成长且充满挑战的创业环境中实现显著职业发展的绝佳机会，具有高度的个人责任感。
Tiger Analytics 向所有申请人和员工提供平等的就业机会，不因种族、肤色、宗教、年龄、性别、性取向、性别认同/表达、怀孕、国籍、祖籍、婚姻状况、受保护的退伍军人身份或残疾而有所区别。
查看英文原文
Tiger Analytics is a fast-growing advanced analytics consulting firm. Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner. We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.
Tiger Analytics is seeking an experienced Senior Data Engineer to join our team, specifically focused on building scalable Generative AI architectures within the AWS ecosystem. You will architect the data foundations that power LLMs and autonomous agents for our Fortune 500 partners.
Key Responsibilities:
- GenAI Infrastructure: Architect data pipelines using Amazon Bedrock and Amazon SageMaker to build, deploy, and scale Generative AI applications.
- Vector Foundations: Implement and optimize vector search capabilities using Amazon OpenSearch Serverless or specialized vector engines for RAG (Retrieval-Augmented Generation).
- Serverless Data Engineering: Build highly scalable, event-driven ETL pipelines using AWS Lambda, AWS Glue, and Amazon Kinesis.
- Modern Data Stack: Manage large-scale data lakehouses leveraging Amazon S3, AWS Lake Formation, and Amazon Redshift.
- LLM Ops: Integrate AWS Step Functions and SageMaker Pipelines to automate the fine-tuning and deployment of foundation models.
Requirements
- Experience: 8-12 years in Data Engineering with a heavy focus on the AWS Cloud stack.
- AWS Expertise: Deep hands-on experience with Glue, Athena, EMR, and Redshift.
- AI/ML Tools: Proficiency in LangChain or LlamaIndex integrated with AWS services to handle unstructured data (text, images, PDFs).
- DevOps & IAC: Experience deploying infrastructure using AWS CDK or Terraform.
- Core Skills: Advanced SQL, Python and PySpark skills tailored for distributed processing on AWS.
Benefits
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy,
national origin, ancestry, marital status, protected veteran status, disability
status, or any other basis as protected by federal, state, or local law.
Originally posted on Himalayas