Utility Systems Data Engineer III

Staff Data Engineer, Applied Data Science IIIEPE is seeking a Staff Data Engineer, Applied Data Science III with demonstrated experience in the electric utility sector to join our consulting practice to support analytical and applied data science, data engineering and data architecture consulting projects with our utility and energy-sector clients.
In this role, you will help EPE's utility clients translate their utility data and business requirements into well-designed data pipelines, reliable models and integrated analytics and data science workflows. Your work will span data discovery, architecture and integration design, hands-on development, machine learning, and model maintenance.
This is a hands-on individual contributor role for a professional with 4–5 years of relevant experience, including experience in the utility sector. You will deliver defined technical components and workstreams, collaborating with senior technical staff and client architects on broader architectural decisions. Depending on the engagement, responsibilities may involve direct implementation or design specifications and advisory support for client-owned implementation.
ResponsibilitiesData Engineering for Utility ClientsExplore, evaluate, and understand utility data sets including advanced metering infrastructure interval (AMI) energy data, SCADA data, GIS data, utility asset data, customer programs data, and billing data.
Design, develop, test, and maintain repeatable ETL/ELT pipelines that ingest, transform, reconcile, and deliver data from databases, files, APIs, and other approved sources.
Develop reusable Python and SQL components for data cleansing, standardization, aggregation, validation, and feature preparation.
Profile source data and establish automated checks for completeness, validity, consistency, uniqueness, and freshness.
Develop record-matching and entity-resolution logic to reconcile customer, asset, device, program, and operational records.
Investigate pipeline failures, data discrepancies, and performance issues; document corrective actions.
Data Architecture and Integration Design for Utility ClientsTranslate business, analytical, and operational requirements into documented data architecture and integration designs for defined solutions or components.
Develop conceptual, logical, and physical data models, entity relationship diagrams, schemas, data dictionaries, and source-to-target mappings.
Design data flows connecting source systems, staging and storage layers, analytical datasets, models, and downstream applications.
Evaluate and document design tradeoffs involving relational databases, warehouses or lakehouses, batch or API integration, performance, scalability, maintainability, and cost.
Incorporate client requirements for data ownership, access controls, sensitive information, retention, lineage, and auditability into solution designs.
Applied Data Science for Utility ClientsIn the context of typical utility data sets (AMI, SCADA, weather, GIS, asset, and customer programs data sets), develop and validate forecasting models, classification schemas, anomaly-detection analysis, clustering approaches, and prioritization models as appropriate to the application.
Address overfitting, class imbalance, sampling bias, and time-dependent validation requirements.
Monitor model performance and support documented recalibration, retraining, and model updates.
Connect analytical models to reliable data pipelines and reproducible datasets, working with client deployment teams where appropriate.
Communicate assumptions, uncertainty, limitations, and the distinction between observed, inferred, and predicted results.
Client and Technical DeliveryParticipate in requirements workshops, technical interviews, architecture reviews, and client working sessions.
Prepare clear technical reports, process maps, design documentation, and analytical presentations.
Deliver assigned work within agreed scope, schedule, budget, and quality expectations.
Identify and escalate data-access constraints, technical dependencies, methodological limitations, and delivery risks.
Contribute to peer reviews, reusable technical methods, and guidance for less-experienced colleagues.
Use AI-assisted development responsibly, validating outputs and protecting client information.
QualificationsBring your passion, here's what's needed:Bachelor's degree in computer science, data science, statistics, mathematics, engineering, or a closely related quantitative discipline.4–5 years of relevant professional experience, including within the utility sector, with hands-on work in data engineering and applied data science, with demonstrated contributions to data architecture or integration design.
Experience building and maintaining repeatable data pipelines or integrations beyond one-time data preparation and exploratory notebooks.
Experience designing data models, schemas, relationships, and source-to-target mappings, and explaining the rationale behind design decisions.
Strong Pyt