Operations Data Engineer: AI-Ready Data Pipelines & PI

southerncompany; Birmingham (AL); On-site; USD 110,000 - 160,000; Full time

Job summary

Southern Power Monitoring & Diagnostics Center in the United States seeks an Operations Data Engineering Analyst to manage PI/IP.21 environments, AF configuration, and real-time dashboards. You will curate AI-ready datasets for Databricks and support AI/ML analytics, ensuring high-quality data for monitoring and predictive models.

Responsibilities include tag management, AF analytics deployment, data quality monitoring, and cross-team collaboration with engineers and analysts to scale data

Qualifications

• Hands-on PI System experience including AF, PI Vision, and interfaces.
• Proficient in Python for data extraction, transformation, and automation.
• SQL proficiency for data querying across databases.
• Experience with Databricks and ETL/ELT pipelines.
• Understanding of data quality, governance, and surrogate data concepts.

Responsibilities

• Support PI/IP.21 system admin tasks: tags, AF, analytics, and vision screens.
• Curate datasets for AI/ML training and deployment in Databricks.
• Validate data quality, handle gaps, and ensure readiness for analytics.
• Collaborate with AI/ML teams and operations to deliver data solutions.
• Monitor pipelines and improve reliability, scalability, and documentation.

Skills

Python

SQL

AVEVA PI System

Databricks

AI/ML data readiness

Education

Bachelor's degree in Engineering, Computer Science, Data Science, or related field

Master's degree in Engineering, Computer Science, Data Science, preferred

Tools

PI DataLink

PI Web API

PI Connect

Databricks / Apache Spark

Job description

Southern Power Monitoring & Diagnostics Center in the United States seeks an Operations Data Engineering Analyst to manage PI/IP.21 environments, AF configuration, and real-time dashboards. You will curate AI-ready datasets for Databricks and support AI/ML analytics, ensuring high-quality data for monitoring and predictive models.

Responsibilities include tag management, AF analytics deployment, data quality monitoring, and cross-team collaboration with engineers and analysts to scale data