Data Engineer: Data Pipelines & AI-Ready Analytics

The Exchange; Dallas (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

The Exchange in Dallas is seeking a data systems manager to design, build, and maintain data pipelines for structured and unstructured data used in AI model training. This role emphasizes data quality, privacy, and collaboration with cross-functional teams.

You will work onsite at HQ in Dallas, addressing data processing efficiency, reliability, and scalable architectures while aligning with enterprise AI initiatives and regulatory requirements.

Qualifications

• Familiarity with Perplexity or similar enterprise AI research platforms.
• Data analytics skills: collecting, cleaning, analyzing and presenting data.
• Research skills: finding, validating and summarizing information from multiple sources.
• Ability to prioritize work and meet deadlines with minimal supervision.
• Strong written and verbal communication skills.

Responsibilities

• Build and maintain data pipelines to ingest, transform, and store structured and unstructured data.
• Optimize data workflows for performance, scalability, and reliability.
• Partner with cross-functional teams to ensure data availability and usability for model training.
• Implement data validation, monitoring, and tracking to ensure data integrity; deliver clean datasets.
• Enforce data privacy, security, and compliance standards.
• Performs other duties as assigned

Skills

Data analytics

Research skills

Communication skills

Prioritization & multitasking

Prompts writing & testing

Education

Bachelor's degree in IT/CS or related

Tools

Power BI

Excel

ServiceNow

Ticketing tools

Job description

The Exchange in Dallas is seeking a data systems manager to design, build, and maintain data pipelines for structured and unstructured data used in AI model training. This role emphasizes data quality, privacy, and collaboration with cross-functional teams.

You will work onsite at HQ in Dallas, addressing data processing efficiency, reliability, and scalable architectures while aligning with enterprise AI initiatives and regulatory requirements.