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# Sr AI Data Engineer (Snowflake, AWS, GenAI)

- **Company:** Connvertex Technologies Inc.
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Microsoft Windows, Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, ARM Architecture, Mobile Application Development, Continuous Integration, Customer Data Management, Data Deduplication, Information Engineering, Amazon DynamoDB, Github, Python (Programming Language), Performance Tuning, Role-Based Access Control, Regression Testing, Scala (Programming Language), SQL Stored Procedures, SQL Databases, Management of Software Versions, Sql Optimization, Large Language Models, Snowflake, State Machines, Generative AI, Cloudformation, Build Management, AI Platforms, Information Technology, Deployment Automation, Drift Detection, Terraform, Human in the Loop, Jenkins, Data Generation
- **Published:** October 29, 2026
- **Apply:** https://www.careerjet.com/jobad/us06dd73f199fda94fe54d5c93febc02e8

## About the Role

8+ years of experience, including 5+ in data engineering
2+ years building and deploying Generative AI applications in production
Strong Snowflake expertise: advanced SQL, Tasks and Streams, stored procedures, performance tuning
Hands-on Snowflake Cortex AI, or an equivalent production LLM platform
Experience with the Snowflake Native App Framework (Snowpark in Python or Scala, packaging, versioning, upgrades, secure distribution)
Strong AWS: S3, Lambda, Glue, Step Functions, DynamoDB, EC2, CloudFormation, Kinesis
Production-grade Python, including testing and packaging
CI/CD (GitHub Actions and/or Jenkins) and IaC (Terraform and/or CloudFormation)
Experience building structured extraction pipelines from free text at scale, with validation, deduplication, and quality controls
ML/LLM observability with Arize or a comparable tool, and a strong grasp of evaluation design, error analysis, and drift monitoring
Ability to work independently against milestone-based outcomes, and to communicate with technical and non-technical stakeholders
Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent practical experience
Preferred
Hospitality, customer profile, personalization, or preference-management experience

## Description

Hyatt's Data Engineering team is looking for a Senior AI Data Engineer to deliver data and AI capabilities for key initiatives. You'll work with Data Product Management and Data Science to build scalable ingestion, transformation, and GenAI-enabled pipelines in Snowflake and AWS. A large part of the work is turning messy free-text customer data into clean, structured, trustworthy data with Snowflake Cortex AI, and packaging it as a Snowflake Native App.
Roles and Responsibilities:
Must have:
Human-in-the-loop workflows and taxonomy-driven classification
Data privacy and compliance for customer data
Snowflake governance and security (RBAC, masking and row access policies, tagging)
Automated regression testing for prompts and LLM outputs
Snowpark Container Services or containerized ML/AI services
Windows task automation and scripting
AWS or Snowflake certifications
Snowflake Cortex AI,
Hyatt's Data Engineering team is looking for a Senior AI Data Engineer to deliver data and AI capabilities for key initiatives. You'll work with Data Product Management and Data Science to build scalable ingestion, transformation, and GenAI-enabled pipelines in Snowflake and AWS. A large part of the work is turning messy free-text customer data into clean, structured, trustworthy data with Snowflake Cortex AI, and packaging it as a Snowflake Native App.
What you'll do
Ingest: Design and build pipelines from multiple source systems into Snowflake using AWS services and Python.
Transform: Build and optimize SQL pipelines that cleanse, normalize, and deduplicate free-form text.
Apply GenAI: Implement Snowflake Cortex AI workflows for classification, extraction, and structured data generation. Build prompt versioning, response parsing, validation, and fallback logic.
Ship Native Apps: Build and deploy Snowflake Native Apps, including permissions, versioning, upgrades, and release workflows, with Snowpark logic and CI/CD for repeatable delivery.
Measure quality: Define and track data and model KPIs (precision, recall, coverage, drift, error rates). Integrate Arize or a comparable platform for observability and root-cause analysis.
Operationalize: Own production rollout, deployment automation, runbooks, documentation, and handoff to internal teams.