Operations Data Engineering Analyst

Southern Company • Birmingham, AL

2d

About The Position

The Operations Data Engineering Analyst is part of Southern Power’s Monitoring & Diagnostics (M&D) Center, which provides remote monitoring and diagnostic support to the company’s gas, solar, wind, and energy storage assets. The role supports operational data infrastructure and AI/ML data readiness by assisting with the operation, maintenance, expansion, and migration of the AVEVA PI and AspenTech IP.21 historian environments. Work includes tag management, Asset Framework configuration, analytics deployment, PI Vision development, data quality monitoring, and remediation.

The analyst extracts, transforms, curates, and delivers clean, structured datasets from PI and IP.21 to downstream data engineering platforms such as Databricks. These datasets support M&D’s AI and machine learning portfolio, including anomaly detection, predictive analytics, and AI agents. The role is responsible for helping ensure data is trusted, high-quality, and ready for real-time monitoring and advanced analytics.

The position works closely with the PI/IP.21 System Administrator, M&D and AI/ML analysts, Renewable Performance Engineers, Renewable, Gas, and Commercial Operations, the Technology Organization, SCADA teams, and plant personnel.

Requirements

• Bachelor’s degree in Engineering (Electrical, Computer, Instrumentation and Controls), Computer Science, Data Science, or a related technical field.
• Two to three years of hands-on experience with AVEVA PI System, including PI Data Archive, PI Asset Framework, PI Vision, and PI Interfaces/Connectors.
• Proficiency in Python for data extraction, transformation, pipeline development, and automation, including Pandas, NumPy, Web API calls, and scripting.
• Proficiency in SQL for querying, integration, and manipulation across relational databases and data platforms.
• Familiarity with Databricks, Apache Spark, or equivalent data engineering platforms, including ETL/ELT pipeline development, Delta Lake, and feature store concepts.
• Experience with data quality monitoring, validation, remediation, and surrogation in an operational or industrial context.
• Ability to make timely decisions, apply sound judgment, and demonstrate personal responsibility.
• Demonstrate Southern Company values: Safety First, Intentional Inclusion, Act with Integrity, and Superior Performance.
• Ability to build productive working relationships and contribute effectively as a team player.
• Detail-oriented and proactive approach to anticipating data quality issues.
• Ability to work independently while contributing to team objectives.
• Creative problem-solving ability and intellectual curiosity to learn tools, platforms, and methodologies.

Nice To Haves

• Master’s degree in Engineering (Electrical, Computer, Instrumentation and Controls), Computer Science, Data Science, or a related technical field.
• Working knowledge of PI DataLink, PI Web API, and PI Connect for data extraction and downstream integration.
• Experience developing and deploying PI AF Analytics, calculated attributes, event frames, and notifications.
• Foundational understanding of machine learning model deployment pipelines, including feature engineering, data labeling, training data requirements, and inference data delivery.
• Experience with Power BI, Tableau, or similar business intelligence tools for dashboard development and operational reporting.
• Experience in power generation, renewables, or utilities operations.
• Experience with other data historians such as AspenTech IP.21, dataPARC, or eDNA.
• Familiarity with Ignition or other SCADA systems.
• Knowledge of OPC DA/UA, DNP3, or Modbus communications protocols and data acquisition architectures.
• AVEVA/OSIsoft PI System accreditation.

Responsibilities

• Support creation, modification, scaling, and decommissioning of PI/IP.21 tags, maintaining fleet-wide naming, point type, and compression standards.
• Create, modify, and delete Asset Framework elements, attributes, templates, and hierarchies, maintaining AF integrity across equipment types and OEMs.
• Develop, test, and deploy PI AF Analytics, including calculated attributes, rollup expressions, and event frames, and validate outputs before production deployment.
• Monitor PI and IP.21 data quality, identify stale tags, flatlined signals, bad values, communication failures, scaling errors, and missing data, and remediate issues proactively.
• Support data surrogation processes when needed.
• Design, develop, maintain, and update real-time PI Vision screens and dashboards for fleet monitoring, equipment health, data quality, and KPI tracking.
• Assist with onboarding generation and storage sites into PI/IP.21, including interface or connector configuration, tag provisioning, AF buildout, initial data validation, and PI Vision screen creation.
• Provide PI/IP.21 data extraction guidance and support to operations, performance, and analytics teams using tools such as PI DataLink, IP.21 Excel Connector, Python, SQL, PI Web API, and PI Connect.
• Help diagnose and resolve data flow issues, interface communication failures, archive gaps, and system errors, escalating infrastructure-level issues as appropriate.
• Build and maintain repeatable data curation pipelines that clean, validate, deduplicate, align timestamps, handle missing values, and resolve data quality issues for AI/ML training and inference datasets in Databricks.
• Partner with AI/ML analysts to translate model input requirements, feature definitions, labeling schemas, data frequency needs, and quality thresholds into data engineering tasks and datasets.
• Validate curated datasets against AI model specifications, including completeness, balance, feature drift, outliers, and schema compliance, and resolve issues before training or inference.
• Monitor pipeline health and performance, alert on failures, latency, or data quality degradation, and improve pipeline efficiency, reliability, and scalability.
• Build and maintain working relationships with technical, operational, analytics, and plant teams.
• Maintain documentation of PI configurations, AF structures, tag inventories, data pipeline architectures, dataset catalogs, and standard operating procedures.
• Apply industry best practices, lessons learned, and feedback; participate in fleet-wide standardization and data governance initiatives.
• Stay current on AVEVA PI System updates, Databricks and data engineering practices, AI/ML pipeline methodologies, and relevant industry trends.

Benefits

• Competitive base salary
• Annual incentive awards for eligible employees
• Health, welfare, and retirement benefits designed to support physical, financial, and emotional/social well-being
• Potential additional compensation, such as an incentive program

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level