Implement automated data quality checks, validation rules, and anomaly detection at each stage of the data pipeline to prevent corrupt, incomplete, or biased data from reaching AI models
Define and enforce data quality SLAs (e.g., completeness, timeliness, consistency, accuracy thresholds)
Build monitoring and alerting systems to detect pipeline failures, data drift, and schema changes that could degrade AI model performance
Conduct root cause analysis on data quality incidents and implement preventative controls
Implement and maintain end-to-end data pipelines, including processes for structured, semi-structured, unstructured datasets
Work with AI Deployment Engineers to optimize data pipelines for AI model training and data stores for AI model retention
Ensure data availability and reliability across operational AI systems
Implement data retention, data lineage, and other data governance standards across various data pipelines and products
Collaborate with both technical and non-technical teams for seamless data integration with automation tools
Implement both batch and real-time/streaming data pipelines to support diverse AI and/or automation use cases

Bachelor’s or Master’s degree in Business Analytics, Data Engineering, Data Science, or another similar field
8+ years of experience in related field, such as Data Engineer, Automation Platform Architect, or the like
Proficiency in Python, SQL, and big data frameworks (Hadoop, Spark)
Familiarity with tools such as Snowflake, dbt, FiveTran, and Tableau
Familiarity with GDPR, CCPA/CPRA, and other data privacy regulations
Understanding of data governance and security compliance, specifically focused on how to maintain at scale with AI solutions
Effective communication to align data processes with business needs
Previous background in management consulting is a plus
Strong business acumen and understanding of enterprise-level processes and the interplay between functions, namely digital, data, and functional teams
Strong attention to detail, a constantly analytical mindset, and the ability to work independently and effectively to deliver value.

AI Data Engineer

Key skills