Data Engineer

Ai Makers; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

Qualifications

• Ability to assess, clean, and prepare data for dependable use by AI applications.
• Experience connecting pipelines to established data sources, including legacy enterprise systems.
• Understanding of corpus structure and quality effects on retrieval and answers.
• Ability to work within client-managed cloud environments (AWS, Google Cloud, or Azure).
• Clear communication skills for discussing data constraints with stakeholders.
• Attention to data sovereignty and regulatory requirements.

Responsibilities

• Audit client data sources and classify quality to determine what must be cleaned.
• Connect pipelines to existing exports and legacy systems.
• Build corpora for RAG applications and test coverage to expose gaps.
• Deploy and operate data systems in client cloud accounts.
• Explain data quality limitations and propose practical next steps to clients.
• Create an automated reporting agent for recurring decision support.

Skills

Data quality assessment

Data cleansing for AI

Pipeline connectivity

Cloud environments (AWS/GC/Azure)

Stakeholder communication

Regulatory/privacy awareness

Job description

Role overview

Prepare and operate client data pipelines that supply AI applications, with a focus on whether the underlying data is usable and trustworthy. You’ll assess source quality, connect to existing systems, build and test retrieval corpora, and deploy within client-controlled cloud environments.

Responsibilities

• Audit client data sources, classify their quality, and identify what must be cleaned before AI systems can rely on them.
• Connect pipelines to existing exports and legacy systems, including sources that clients cannot readily replace.
• Build corpora for RAG applications and test their structure and coverage to expose gaps that could lead to unreliable answers.
• Deploy and operate data systems in client cloud accounts, adapting to the environment provided.
• Explain data quality limitations to clients and recommend practical next steps when issues block delivery.
• Create an automated reporting agent that delivers recurring decision support without manual intervention.

Requirements

• Ability to assess, clean, and prepare data for dependable use by AI applications.
• Experience connecting pipelines to established data sources, including legacy enterprise systems.
• Understanding of how corpus structure and quality affect retrieval and generated answers.
• Ability to work within client-managed cloud environments, including AWS, Google Cloud, or Azure.
• Clear communication skills for discussing data constraints and recommendations with client stakeholders.
• Attention to data sovereignty and relevant privacy and regulatory requirements.