AI-Powered Data Quality Engineer Intern

Affinity Solutions; United States; On-site; USD 34,000 - 41,000; Part time

Job summary

Affinity Solutions is seeking a motivated Data Quality AI Engineer Intern to join our Data Engineering team. You will work on production-scale data platforms to build automated data quality solutions, AI-powered tools and scalable data pipelines.

Responsibilities include developing QA frameworks for datasets and AI outputs, building profiling and anomaly-detection workflows with Python, SQL, Spark and cloud technologies, and monitoring model outputs for drift and reliability.

Qualifications

• Strong SQL skills including joins, CTEs and window functions.
• Proficiency in Python for automation and data processing.
• Experience with data warehouses and cloud platforms.
• Experience with REST APIs and version control (Git).
• Strong analytical, problem-solving and communication skills.

Responsibilities

• Develop and automate AIML data quality frameworks to validate datasets, features, embeddings and AI outputs.
• Build data profiling, validation, reconciliation and anomaly detection workflows using Python, SQL, Spark and cloud technologies.
• Implement quality checks for LLM and Generative AI applications including prompt validation and model output monitoring.
• Monitor AI data pipelines for data drift, schema changes, completeness and consistency.
• Collaborate with Data Engineering and ML teams to design scalable QA frameworks, dashboards, alerts and automated testing solutions.

Skills

SQL

Python

Analytical skills

Communication skills

Education

BS/MS in Computer Science, Data Science, Engineering, Information Systems, or related field

Tools

Snowflake

Redshift

BigQuery

Databricks

AWS

Azure

GCP

REST APIs

Git

Job description

Affinity Solutions is seeking a motivated Data Quality AI Engineer Intern to join our Data Engineering team. You will work on production-scale data platforms to build automated data quality solutions, AI-powered tools and scalable data pipelines.

Responsibilities include developing QA frameworks for datasets and AI outputs, building profiling and anomaly-detection workflows with Python, SQL, Spark and cloud technologies, and monitoring model outputs for drift and reliability.