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).

Our client is a leading Banking and Financial Industry, and we are currently interviewing to fill this and other similar contract positions. If you are interested in this position, please apply online for immediate consideration.

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