Lead Data Engineer - AWS

* 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

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:

* 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.

* 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.

* 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

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.

* 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).

* 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.

* 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.

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).