Senior Data Engineer
Austin, Minnesota, United States of America
Contract
3 hours ago
Key skills
PythonSQLAIMLGenerative AILLMRAGAgenticscikit-learnPandasMLOpsETLELTData EngineeringSnowflakeAirflowdbtTerraformGitHub ActionsGitHubCI/CDLeadershipCollaboration
About this role
Role Overview
As the Senior Data Engineer, you will be the technical visionary and hands-on architect for our enterprise AI initiatives. You will leverage the full Snowflake AI Data Cloud stack to design, build, and scale production-grade Generative AI applications, RAG (Retrieval-Augmented Generation) frameworks, and agentic workflows. You will bridge the gap between complex data engineering and advanced LLM implementation, ensuring our AI solutions are secure, governed, and high-performing.
Key Responsibilities
AI Architecture & Strategy: Lead the design of end-to-end AI solutions using Snowflake Cortex AI, including the implementation of LLM functions (AI_COMPLETE, AI_SUMMARIZE, etc.) and Cortex Search.
RAG & Document Intelligence: Build and optimize RAG pipelines that ground LLMs in enterprise data. Implement Document AI for extracting value from unstructured data (PDFs, images, etc.).
Advanced Data Engineering: Design scalable ELT/ETL pipelines using Snowpark (Python) and dbt. Oversee the creation of vector embeddings and the management of Vector Data Types within Snowflake.
Agentic Workflows: Develop "Agentic" AI systems that can reason through complex tasks and interact with enterprise datasets via natural language.
Governance & Security: Enforce Snowflake Horizon standards, ensuring all AI models and data products comply with RBAC, data masking, and differential privacy policies.
Mentorship & Leadership: Lead a squad of 3 5 engineers. Conduct code reviews, define best practices for AI/MLOps, and drive the technical roadmap in collaboration with stakeholders.
Technical Requirements
Category
Must-Have Skills
Snowflake Core
SnowPro Core/Advanced, Snowpark, Streams & Tasks, Dynamic Tables.
AI/ML Stack
Snowflake Cortex, Vector Search, ML Functions (Forecasting, Anomaly Detection).
Languages
Expert-level SQL and Python (specifically pandas, scikit-learn, and Snowpark).
AI Concepts
RAG architecture, Prompt Engineering, LLM Evaluation (metrics like faithfulness/relevancy).
Orchestration
Experience with Airflow or Snowflake-native orchestration; CI/CD via GitHub Actions/Terraform.