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# Data Engineer

- **Company:** SGA Inc.
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Application Frameworks, ARM Architecture, Encodings, Databases, Information Engineering, Data Infrastructure, Python (Programming Language), Machine Learning, Standard Sql, Search Technologies, Unstructured Data, Management of Software Versions, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Snowflake, Llamaindex, Build Management, Data Lineage, Machine Learning Operations, Data Pipelines
- **Published:** October 2, 2026
- **Apply:** https://www.dice.com/job-detail/aca66882-b715-4187-b7da-a39dfecfbf8e

## About the Role

* 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

Preferred Skills:

 * 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)

## Description

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., * 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