AI Data Engineer

Cloud Analytics Technologies, LLC; Dallas (TX); On-site; USD 90,000 - 130,000; Full time

Job summary

Cloud Analytics Technologies, LLC is seeking a Data Engineer to design and manage data pipelines powering AI/ML initiatives. You will ingest, clean, and transform data for traditional analytics and AI workloads, including vector embeddings and RAG architectures.

You will collaborate with ML engineers to define data requirements for training and evaluation, ensure data lineage and governance, and optimize pipelines for cost and performance.

Qualifications

• Strong SQL and Python/Scala for data pipelines.
• 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.

Responsibilities

• Design and build pipelines to ingest, clean, and transform data for AI/ML use cases.
• Build and maintain vector embedding pipelines and support vector DB integration.
• Develop feature stores/pipelines for ML model training and inference.
• Implement data pipelines supporting RAG architectures with metadata tagging.
• Partner with ML teams to understand data requirements for model training.
• Ensure data lineage, quality, and governance across AI pipelines.
• Optimize pipelines for cost and performance (e.g., Dynamic Tables, streaming).
• Support MLOps/LLMOps workflows — versioning datasets for reproducibility.

Skills

SQL

Python/Scala

Data engineering

Cloud data platform

Airflow

dbt

Embedding models

Vector search

RAG patterns

Unstructured data

ML lifecycle

Tools

Airflow

dbt

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other

Job Description:

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, Llama Index)

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.

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