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# AI Data Engineer

- **Company:** EApps Tech LLC
- **Location:** Raleigh, NC, United States
- **Experience:** Expert
- **Salary:** $104,000.0 - $124,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, ARM Architecture, Profiling, Data Validation, Information Engineering, Data Security, Python (Programming Language), Performance Tuning, Reference Data, Strategies of Testing, Snowflake, Agentic-AI, Data Layers, Data Lineage, Semantic Modeling
- **Published:** October 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=14b30aec029a3ce7

## About the Role

* Healthcare or payer domain experience, especially with member, claims, provider, quality, or reference data.
 * Snowflake SQL: Highly skilled. Must be able to develop complex transformations, optimize performance, and work confidently with CTEs, advanced joins, and analytic/window functions.
 * Snowflake Data Engineering: Strong real-world experience building curated data layers using medallion architecture principles across bronze, silver, and gold datasets.
 * Semantic Modeling / Semantic Layer Engineering: Must be able to define business-friendly metrics, dimensions, grain, relationships, and reusable semantic views on top of Snowflake data.
 * Snowflake Cortex / AI-Enabled Analytics: Experience supporting Snowflake Cortex-based analytics workflows, semantic intelligence, or agentic analytics use cases is strongly preferred.
 * Data Quality, Profiling, and Validation: Must be able to identify natural and surrogate keys, assess cardinality and distribution, validate transformations, and ensure trustworthy semantic outputs.
 * Strong communication and ownership: Able to work across engineering, product, and analytics partners, clarify requirements, and proactively drive work with limited direction.

"Desirements"

 * Experience in evaluating / troubleshooting and measuring agent performance, identifying issues, and optimizing it
 * Experience with dbt, semantic view design, or governed self-service analytics patterns.
 * Python experience for automation, metadata processing, data validation, or AI-assisted engineering workflows.
 * Exposure to agentic AI, natural language analytics, or Snowflake Cortex Analyst / Cortex Search use cases.

Specific technologies to be used and level of proficiency :

 * Snowflake SQL: Highly skilled to expert level.
 * Snowflake: Strong hands-on experience with performance tuning, secure data design, and curated data modeling.
 * Semantic Modeling / Semantic Views: Strong proficiency in defining business metrics, dimensions, relationships, grain, and reusable semantic abstractions.
 * dbt: Preferred real-world experience developing modular transformations, tests, and deployment patterns.
 * Snowflake Cortex: Preferred experience with Cortex-enabled analytics, semantic intelligence, or agent-supporting workflows.
 * Python: Some experience desired, with preference for intermediate or higher skill for automation and validation use cases.

## Description

Day to day Job Description of a Senior Data AI Engineer role:

 * Design and implement scalable Snowflake data models and pipelines that align with business objectives and agentic AI use cases.
 * Build curated bronze, silver, and gold data layers and reusable semantic assets that support trusted analytics and natural-language data access.
 * Collaborate with product, analytics, and engineering partners to translate complex requirements into governed data products and semantic models.
 * Optimize Snowflake queries, warehouse configurations, and transformation performance for reliability, scalability, and cost efficiency.
 * Design and implement data quality validation, profiling, and testing strategies to improve trust in downstream semantic outputs.
 * Contribute to lineage-aware transformation patterns, documentation, and engineering standards for reusable data assets.
 * Use dbt where applicable to develop modular transformations, tests, and deployment patterns; prior hands-on dbt experience is preferred.
 * Support Snowflake Cortex-enabled analytics, semantic intelligence, and agent-assisting workflows where applicable.
 * Troubleshoot production issues and implement durable long-term solutions.
 * Document architectural decisions, semantic definitions, and data lineage.