Data Engineering, Architecture & Applied Data Science, III

Overview

We are designing the grid of the future!

EPE is seeking a Staff Engineer III with demonstrated experience in data engineering, data architecture, and applied data science to support utility and energy-sector consulting engagements. You will help clients translate fragmented data and complex business requirements into well-designed data models, reliable data pipelines, integrated workflows, and statistically rigorous analytical solutions. 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. You will independently 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.

Responsibilities

How you can make an impact:

Data Engineering

• Design, develop, test, and maintain repeatable ETL/ELT pipelines that ingest, transform, reconcile, and deliver data from databases, files, APIs, and other approved sources.
• Using agentic workflows, Develop reusable Python and SQL components for data cleansing, standardization, aggregation, validation, and feature preparation.
• Implement scheduling, dependency management, logging, failure handling, and monitoring for assigned data workflows.
• 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.

Data Architecture and Integration Design

• 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.
• Define identifiers, relationships, reference data, authoritative sources, reconciliation rules, and requirements for retaining historical changes.
• Design data flows connecting source systems, staging and storage layers, analytical datasets, models, and downstream applications.
• Document data lineage and requirements for ownership, access, retention, and auditability, and prepare implementation-ready specifications and handoff documentation.

Applied Data Science

• Apply probability, statistical inference, regression, hypothesis testing, and uncertainty quantification to real-world client problems.
• Develop and validate forecasting, classification, anomaly-detection, clustering, and prioritization models as appropriate to the application.
• Perform feature engineering, model selection, tuning, and evaluation against meaningful baselines.
• Address data leakage, overfitting, class imbalance, sampling bias, and time-dependent validation requirements.
• Monitor deployed models, investigate false positives and false negatives, and support recalibration and retraining.

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.

Qualifications

Bring 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 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, including testing, scheduling or orchestration, logging, and data-quality validation, beyond one-time data preparation or 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 complex queries, data transformation, relational databases, structured and semi-structured data, and APIs/JSON.
• 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 distributions, conditional 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.
• Hands-on experience with pandas, NumPy, scikit-learn, and statistical libraries such as SciPy or statsmodels.
• 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 AI / agentic coding workflow processes and automating workflows and analytical processes.
• Experience with Databricks, Spark/PySpark, Delta Lake, 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.
• Master’s 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 for you
• 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 will be located in City, State

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 from third-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 agency. EPE will not pay a fee to a third-party recruiter or agency without a previously signed third-party agreement and has not coordinated their recruiting activity with the appropriate member of the Talent Acquisition team.