Tiger Analytics
Lead Data Engineer- AWS
Remote Data Engineering role with clear candidate location fit.
PostedJun 21, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
USA
Role overview
Lead Data Engineer- AWS
Requirements and responsibilities
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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.
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