Note: The job is a remote job and is open to candidates in USA. Dice is a company seeking a Data Engineering & AI Workflow Specialist. The role involves designing and maintaining ETL data pipelines, collaborating with teams to integrate AI solutions, and ensuring data quality for AI/ML models.

Responsibilities

• Design, build, and maintain scalable ETL data pipelines to ingest, transform, and deliver data across systems
• Work with structured and unstructured data sources to support downstream analytics and AI use cases
• Collaborate with engineering and product teams to integrate AI-driven solutions into existing platforms
• Develop and refine AI development workflows, including experimentation, evaluation, and deployment support
• Support data preparation, feature engineering, and validation for AI/ML models
• Ensure data quality, consistency, and performance across pipelines
• Assist in automating processes and improving operational efficiency
• Document technical designs, workflows, and implementation details for maintainability and handoff

Skills

• Strong experience with ETL/data processing (Python)
• Experience building and maintaining data pipelines in cloud or distributed environments
• Familiarity with AI/ML development workflows
• Ability to work with large datasets and optimize performance
• Strong problem-solving and debugging skills
• Bachelors degree in Computer Science, Information Systems, Engineering, or a related technical field
• Equivalent practical experience in data engineering or AI/ML workflows may be considered in lieu of a degree
• 5+ years of experience in data engineering, ETL development, or related technical roles
• 1+ years of experience working with or supporting AI/ML development workflows (e.g., data preparation, model lifecycle, or AI integration into applications)
• Exposure to enterprise AI tooling (e.g., Copilot, LLM orchestration, prompt workflows, or similar)
• Experience working in regulated or enterprise data environments
• Prior experience supporting cross-functional teams (product, engineering, data science)

Company Overview

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