Data Engineer Ai

Job Title - Data Engineer /AI

Location - Remote USA

Experience - Min 10+ years

Skill Requirement :
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.

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.