Staff Data Engineer, Applied Data Science III

We are designing the grid of the future!

EPE 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 EPEs 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 45 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.

Howyou can make an impact:

Data Engineering for Utility Clients

• Explore 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 Clients

• Translate 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 Clients

• In 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 Delivery

• Participate 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.

Bring your passion heres whats needed:

• Bachelors degree in computer science data science statistics mathematics engineering or a closely related quantitative discipline.
• 45 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 Python and SQL proficiency including maintainable programming complex queries data transformation and troubleshooting.
• Experience with pipeline testing scheduling or orchestration logging data-quality validation and failure investigation.
• Working experience with at least one cloud or distributed-data platform such as Databricks Azure AWS Google Cloud or an equivalent environment.
• Strong understanding of probability sampling statistical inference regression confidence and prediction intervals and uncertainty.
• Practical experience developing and evaluating statistical or machine learning models including appropriate validation and interpretation of results.
• Experience with Git code review testing technical documentation and reproducible analyses.
• Ability to communicate technical designs analytical findings assumptions and limitations to technical and nontechnical audiences.

Preferred Qualifications

• Experience with Databricks Spark/PySpark or comparable distributed-data technologies.
• Experience with dimensional modeling warehouse/lakehouse design metadata management or data catalogs.
• Experience with workflow orchestration CI/CD model versioning experiment tracking or model monitoring.
• Experience with time-series or probabilistic forecasting Bayesian methods simulation or optimization.
• Familiarity with XGBoost LightGBM PyMC PyTorch or TensorFlow without an expectation of expertise in every framework.
• Experience with utility datasets DER registries load forecasting sensor analytics or engineering automation.
• Masters degree in a relevant quantitative or technical discipline.

Be a part of an innovative team shaping the grid of the future through advanced energy intelligence. For more than half a century Electric Power Engineers (EPE) has partnered with power and energy clients across the globe providing consulting expertise and energy intelligence software solutions for complex engineering and grid modeling challenges. As leaders in the renewables space we are focused on building a modern secure and resilient grid. Join us in making an impact on the communities we serve and the environment in which we live. Together we can transform the future of energy.

How we support you:

• Comprehensive health and wellness benefits including medical dental and vision with 100% premium coverage foryou
• Generous PTO and paid holidays
• MyShare Employee Ownership Program
• Work with industry leaders
• 401K up to a 4% match (100% vested from day 1)

Location: This position remote within the U.S.

Travel: Occasional travel may be needed (10% or less)

EPE is an equal opportunity/AA/Disability/Veteran employer. The EEO is the Law poster and its supplement are available using the following links:EEOC is the Law Poster

Third-Party Recruiting Notification

EPE does not accept unsolicited resumes fromthird-party recruiters. Any unsolicited third-party resumes forwarded by recruiters to EPE via our career page or to any of our managers or employees will be considered public information may be treated as a direct application from the person identified in the resume and will not be eligible for placement fee payment to the will not pay a fee to a third-party recruiter or agencywithout a previously signed third-party agreementand has not coordinated their recruiting activity with the appropriate member of the Talent Acquisition team.

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