Operations Data Engineering Analyst

OPERATIONS DATA ENGINEERING ANALYST

JOB SUMMARY

ThispositionwillfunctionaspartoftheSouthernPowerMonitoring&Diagnostics(M&D)Center,which

providesremotemonitoringanddiagnosticsupporttotheSPCfleetofgas,solar,wind,andenergystorage assets.

TheOperationsDataEngineeringAnalystsupportsbothoperationaldatainfrastructureandAI/MLdata readiness. The role assists the PI System Administrator with daily operation, maintenance, and expansion/migration of SPC’s AVEVA PI andAspenTech IP.21 historianenvironments, including tag

management,AssetFramework(AF)configuration,analyticsdeployment,PIVisiondevelopment,dataquality monitoring, and proactive remediation. The role also extracts, transforms, curates, and delivers clean,

structureddatasetsfromPIandIP.21intodownstreamdataengineeringplatformssuchasDatabricksto supportM&D’sgrowingAIandmachinelearningportfolio,includingiSightanomalydetection,predictive analytics models, and AI agents.

Inshort,thisroleownsthedatajourneyfromrawsensoringestioninPIthroughcurated,AI-readydatasets

-ensuringtheM&Dteamalwayshastrusted,high-quality,well-engineereddatatopowerbothreal-time monitoring and advanced analytics.

ThispositionwillcloselyinterfacewiththeAvevaPI/AspenTechIP.21SystemAdministrator,M&DAnalysts, AI/MLAnalysts,RenewablePerformanceEngineers,Renewable,Gas,andCommercialOperations,theSPC Technology Organization, SCADA teams, and plant personnel.

MAJOR JOBRESPONSIBILITIES

PI/IP.21SystemSupport

• TagManagement- SupportthePI/IP.21Administratorinthecreation,modification,scaling,and

decommissioningofPI/IP.21tagsacrossgas,solar,wind,andenergystorageassets.Ensuretagnaming conventions, point types, and compression settings are consistent with fleet-wide standards.

• AssetFramework (AF)Configuration- Create,modify,anddeleteAFelements,attributes,templates, and hierarchies to reflect fleet changes (new sites, equipment swaps, decommissions). Maintain AF structureintegrityacrossallOEMs(solarinverters,windturbines,gasturbines,batteries,andBOP equipment).

• AFAnalyticsDeployment- Develop,test,anddeployPIAFAnalytics(calculatedattributes,rollup expressions, eventframes)in testandproductionenvironments. Validate analytic outputsagainst expected results prior to production deployment.

• DataQualityMonitoring&ProactiveRemediation- ContinuouslymonitordataqualityacrossPIand IP.21 platforms - identifying stale tags, flatlined signals, bad values, communication failures, scaling

  errors,andmissingdata.Proactivelyremediateissuesbeforetheyimpactdownstreamanalytics, dashboards, or AI models. Support data surrogation processes where needed.

• PI Vision Development & Maintenance - Design, develop, and maintain real-time PI Vision screens and dashboardsforfleetmonitoring,equipmenthealth,dataquality,andKPItracking.Updatedisplaysasnew sites are onboarded or fleet configurations change.

• SiteOnboardingSupport- AssistthePI/IP.21Administratorwithintegratingnewgenerationandstorage sites into the PI/IP.21 System, including interface/connector configuration, tag provisioning, AF buildout, initial data validation, and PI Vision screen creation.

• Data Query Support for Other Teams - Serve as a resource for operations, performance, and analytics teamswhoneedtoextractdatafromPI/IP.21.ProvidesupportandguidanceusingPIDataLink,IP.21Excel Connector, Python, SQL, PI Web API, and PI Connect to enable self-service data access and integration

withdownstreamtools.

• Troubleshooting- Assistindiagnosingandresolvingdataflowissues,interfacecommunicationfailures, archivegaps,andsystemerrorswithinthePIandIP.21environments.Escalateinfrastructure-levelissues to the PI/IP.21 Administrator or IT as appropriate.

DataEngineering&AI/MLDataReadiness

• DatasetCuration&Cleaning- Buildandmaintainrepeatabledatacurationpipelinesthatclean, validate,deduplicate,aligntimestamps,handlemissingvalues,andresolvedataqualityissues-

producingtrusted,well-documenteddatasetsforAI/MLmodeltrainingandinferenceinDatabricks.

• AI/ML Data Requirements Engineering - Partner directly with theAI/MLAnalyst tounderstand model inputrequirements,featuredefinitions,labelingschemas,datafrequencyneeds,andqualitythresholds.

Translatetheserequirementsintoactionabledataengineeringtasksanddeliverdatasetsthatmeet model specifications.

• Model-ReadyDataValidation- ValidatecurateddatasetsagainstAImodelinputspecifications-

checkingforcompleteness,balance,featuredrift,outliers,andschemacompliancein/outsideDatabricks. Flag and resolve data issues before they enter training or inference pipelines.

• PipelineMonitoring&Maintenance- Monitorhealthandperformanceofdatapipelines,alertingon failures, latency, ordata quality degradation.Continuously improve pipeline efficiency, reliability, and scalability as the fleet and AI use cases grow.

Collaboration&ContinuousImprovement

• Cross-FunctionalCollaboration- BuildandmaintainstrongworkingrelationshipswiththePI/IP.21

Administrator,AI/MLAnalysts,M&DAnalysts,RenewablesPerformanceEngineers,Renewable,Gas,and Commercial Operations, SCADA teams, Technology Organization developers, and plant personnel.

• Documentation- MaintainthoroughdocumentationofPIconfigurations,AFstructures,taginventories, data pipeline architectures, dataset catalogs, and standard operating procedures.

• BestPractices&LessonsLearned- Incorporateindustrybestpractices,lessonslearned,andfeedback into daily operations. Participate in fleet-wide standardization efforts and data governance initiatives.

• ContinuousLearning- StaycurrentonAVEVAPISystemupdates,dataengineeringbestpractices (Databricks), AI/ML pipeline methodologies, and relevant industry trends.

JOB REQUIREMENTS

Education

• Bachelor'sdegreeinEngineering(Electrical,Computer,InstrumentationsandControls),Computer Science, Data Science, or a related technical field - required.

• Master'sdegreeinEngineering(Electrical,Computer,Instrumentation&Controls),ComputerScience, Data Science, or a related technical field - preferred.

Experience

Required

• PISystemHands-On: 2-3yearsofhands-onexperiencewithAVEVAPISystem,includingPIDataArchive, PI Asset Framework (AF), PI Vision, and PI Interfaces/Connectors.

• Python: ProficientinPythonfordataextraction,transformation,pipelinedevelopment,andautomation (Pandas, NumPy, Web API calls, scripting).

• SQL: ProficientinSQLfordataquerying,integration,andmanipulationacrossrelationaldatabasesand data platforms.

• DataEngineeringPlatform: FamiliarwithDatabricks,ApacheSpark,orequivalentdataengineering platforms - including ETL/ELT pipeline development, Delta Lake, and feature store concepts.

• DataQuality&Governance: Experiencewithdataqualitymonitoring,validation,remediation,and surrogation in an operational/industrial context.

Preferred

• PIDataAccessTools: WorkingknowledgeofPIDataLink,PIWebAPI,andPIConnectfordataextraction and downstream integration.

• PIAFAnalytics: ExperiencedevelopinganddeployingAFAnalytics,calculatedattributes,eventframes, and notifications.

• AI/MLPipelineUnderstanding: FoundationalunderstandingofMLmodeldeploymentpipelines- includingfeatureengineering,datalabeling,modeltrainingdatarequirements,andinferencedata delivery.

• DataVisualization/BI: ExperiencewithPowerBI,Tableau,orsimilarBItoolsfordashboarddevelopment and operational reporting.

• EnergySector: Experienceinpowergeneration,renewables,orutilitiesoperations.

Plus

• OtherData Historians: ExperiencewithAspenTechIP.21, dataPARC,eDNA etc.

• SCADASystems: FamiliaritywithIgnition/otherSCADAsystems.

• CommunicationsProtocols: OPCDA/UA,DNP3,Modbusprotocolsanddataacquisitionarchitectures.

• PIAccreditation: AVEVA/OSIsoftPISystemaccreditation.

BEHAVIORALATTRIBUTES

• Mustbeabletomaketimelydecisions,applyseasonedjudgment,anddisplaypersonalresponsibility.

• DemonstrateSouthernCompanyValues:SafetyFirst,IntentionalInclusion,ActwithIntegrity,and Superior Performance.

• Beateamplayer-abletoquicklybuildgood,productiveworkingrelationshipswiththePI Administrator, AI/ML team, operations teams, and technology partners.

• Detail-orientedandproactive-anticipatedataqualityissuesbeforetheyimpactdownstream consumers.

• Results-orientedself-starterthatcanworkindependentlywhilecontributingtoteamobjectives.

• Creativelysolveissues,thinkoutsidethebox,andfindinnovativewaystobridgeoperationaldataand AI/ML requirements.

• Intellectuallycurious-eagertolearnnewtools,platforms,andmethodologiesacrossbothPISystem administration and data engineering domains.