Data Engineer (AI & LLM Expertise)

• Maintain and enhance dbt models across staging (DL2) and mart (DL3) layers.
• Monitor and triage dbt Cloud job failures, diagnosing root causes using run logs.
• Support Control-M scheduling changes and DBT deployments.
• Update dev_env.tfvars / Terraform configs for dbt Cloud environment and credential changes.
• Write and maintain SQL models following SQLFluff lint standards (ruff, mypy for Python).
• Manage source freshness and data quality test failures.
• Contribute to GitLab MR reviews and follow CI/CD pipeline (Slim CI ? CD ? Prod deployment via migration tags).
• Submit and track Client (SNOW) requests for Client access, service accounts, and infra changes.
• Apply knowledge of LLM architecture and principles (e.g., tokenization, embeddings) to data preparation and feature engineering for AI/ML projects.
• Develop and maintain robust Python code for ETL processes, with an understanding of how data is processed by LLMs.
• Assist in evaluating and optimizing LLM outputs, considering factors like token limits and response relevance.
• Document AI/ML data pipelines, including explanations of tokenization strategies and data transformations relevant to LLM processing.

• Must have skills: Data Engineer", "Snowflake SQL , "DBT", "Git", "Github Copilot", "AI .
• SQL (Client dialect), dbt Core/Cloud basics.
• Basic Python (scripting, pytest), with a strong understanding of its application in data pipelines and AI/ML.
• Git / GitLab branching and MR workflows .
• Familiarity with Terraform (reading/editing .tfvars).
• Understanding of incremental models, source freshness, and dbt state: selectors.
• Control-M scheduling concepts.
• Client warehousing (sizing, multi-clustering).
• dbt Cloud CLI, Poetry.
• Github Copilot, Claude Code
• Foundational understanding of how LLMs work, including concepts like tokenization, embeddings, and context windows.
• Practical experience or strong theoretical knowledge of applying AI/LLMs to data processing tasks.
• Ability to understand and work with unstructured and semi-structured data for AI applications.
• Familiarity with prompt engineering principles for interacting with LLMs.
• Strong analytical and problem-solving skills, with a proactive approach to learning new AI technologies.
• US military Background is required.
• Experience with vector databases and embedding pipelines.
• Familiarity with MLOps principles and tools.
• Exposure to cloud platforms (AWS, Google Cloud Platform, Azure).
• Experience in building and optimizing data pipelines for AI workloads.
• Understanding of AI model deployment and monitoring.

Pay Range: $55 - $60/hour. Employee benefits include, but are not limited to, health insurance (medical, dental, vision), 401(k) plan, and paid sick leave (depending on work location).

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