Lead Data Engineer - AWS

• 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.

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