Data Engineer Experienced

Data Engineer - AI Platforms, Data Engineering

Pay Rate: $63.00 Per Hour W2
Duration: 12 Month Contract
Location: REMOTE

Position Details

• Work Arrangement: 100% Remote
• Business Hours: 9-5pm EST
• Extension and/or Conversion Opportunity: Yes.

• Strong SQL + one of Python/Scala for pipeline development, experience with orchestration tools and understanding of AI models.

• Strong sql + Python understanding, AI data skills and API implementation.

• Snowflake, dbt, Python

• Interview Process: 2-3 rounds

Summary:
We're looking for a Data Engineer to build and maintain the data infrastructure powering our AI/ML initiatives — including data pipelines for model training, retrieval-augmented generation (RAG), and production ML systems. This role sits at the intersection of data engineering and applied AI, ensuring high-quality, well-governed data is available for both traditional analytics and AI consumption.

Key Responsibilities:

• Design and build pipelines to ingest, clean, and transform structured and unstructured data (documents, PDFs, transcripts, logs) for AI/ML use cases

• Build and maintain vector embedding pipelines and support vector database integration

• Develop feature stores/pipelines for ML model training and inference

• Implement data pipelines supporting RAG architectures — chunking strategies, embedding refresh cycles, metadata tagging

• Partner with ML engineers/data scientists to understand data requirements for model training and evaluation

• Ensure data lineage, quality, and governance across pipelines feeding AI systems

• Optimize pipelines for cost and performance (e.g., Dynamic Tables, streaming ingestion)

• Support MLOps/LLMOps workflows — versioning datasets, tracking data used in model training for reproducibility and compliance

• Implement entitlement/access control logic so AI systems only access data they're authorized to use

Required Skills/Experience:

• Strong SQL + one of Python/Scala for pipeline development

• Experience with a cloud data platform

• Experience with orchestration tools (Airflow, dbt)

• Understanding of embedding models, vector search, and RAG patterns

• Experience with unstructured data processing

• Familiarity with ML lifecycle concepts

Nice to Have:

• Experience with Snowflake Cortex, Cortex Search, or similar in-warehouse AI tooling

• Experience in a regulated industry (financial services) with data entitlement/compliance requirements

• Exposure to LLM application frameworks (LangChain, LlamaIndex)