Lead Data Engineer - AWS

Responsibilities

Architect and deploy scalable data pipelines and infrastructure for Generative AI applications using AWS services. Manage large-scale data lakehouses and automate the fine-tuning and deployment of foundation models.

Requirements

Requires 8-12 years of experience in data engineering with deep expertise in the AWS cloud stack. Proficiency in Python, PySpark, SQL, and AI/ML tools like LangChain or LlamaIndex is essential.

Skills

• AWS
• Data Engineering
• Generative AI
• Amazon Bedrock
• Amazon SageMaker
• Python
• PySpark
• SQL
• Amazon OpenSearch
• ETL
• AWS Glue
• LangChain
• LlamaIndex
• Terraform
• AWS CDK
• Data Lakehouse

About this role

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.
* 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.
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.

About Tiger Analytics Inc.

Business Consulting and Services · 5,001-10,000 employees · headquartered in Santa Clara, CA · founded 2011