Staff Data Engineer, Applied Data Science III

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Engineer, Applied Data Science III based in the United States.

The Staff Data Engineer will support the development of advanced data engineering and applied data science solutions for clients across the utility and energy sectors. This hands-on role combines data pipeline development, data architecture, statistical modeling, and machine learning to turn complex utility data into actionable intelligence. You will work with diverse datasets, including smart meter readings, grid operations, geographic information systems, and customer information. Collaborating with client teams and senior technical experts, you will design reliable data workflows and contribute to scalable analytical solutions. Your work will help utilities improve forecasting, operational insights, and data-driven decision-making. This is an opportunity to contribute to modernizing energy infrastructure and building a more resilient, efficient, and sustainable power grid.

You will deliver technical workstreams across data engineering, architecture, and applied data science, translating client requirements into reliable data solutions and analytical models while ensuring quality, scalability, and clear technical communication.

• Explore and assess utility datasets, including advanced metering infrastructure (AMI) interval data, SCADA, GIS, asset information, customer programs, and billing records.

• Design, develop, test, and maintain repeatable ETL/ELT pipelines that ingest, transform, reconcile, and deliver data from databases, files, APIs, and other approved sources.

• Build reusable Python and SQL components for data cleansing, standardization, aggregation, validation, and feature preparation.

• Profile source data and implement automated quality checks covering completeness, validity, consistency, uniqueness, and freshness.

• Develop record-matching and entity-resolution logic to reconcile customer, asset, device, program, and operational records.

• Troubleshoot pipeline failures, data inconsistencies, and performance issues, documenting findings and corrective actions.

• Translate business, analytical, and operational requirements into documented data architecture and integration designs.

• 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 architectural trade-offs involving relational databases, warehouses, lakehouses, batch processing, APIs, scalability, performance, maintainability, and cost.

• Incorporate client requirements for data ownership, access controls, sensitive information, retention, lineage, and auditability into solution designs.

• Develop and validate forecasting models, classification approaches, anomaly detection, clustering methods, and prioritization models using utility datasets.

• Address modeling challenges such as overfitting, class imbalance, sampling bias, and time-dependent validation.

• Monitor model performance and support documented recalibration, retraining, and ongoing model updates.

• Connect analytical models to reliable data pipelines and reproducible datasets, coordinating with client deployment teams where appropriate.

• Clearly communicate model assumptions, uncertainty, limitations, and the distinction between observed, inferred, and predicted results.

• Participate in requirements workshops, technical interviews, architecture reviews, and client working sessions.

• Prepare technical reports, process maps, architecture documentation, and analytical presentations.

• Deliver assigned work within agreed scope, schedules, budgets, 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 tools responsibly, validating generated outputs and protecting confidential client information.

Requirements:

The ideal candidate combines practical data engineering expertise with applied statistical and machine learning experience, a solid understanding of utility-sector data, and the ability to communicate technical solutions effectively to diverse stakeholders.

• 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 experience in the utility sector, with hands-on contributions to data engineering, applied data science, and data architecture or integration design.

• Demonstrated experience building and maintaining repeatable data pipelines and integrations beyond one-time data preparation or exploratory notebooks.

• Experience designing data models, schemas, relationships, and source-to-target mappings, with the ability to explain architectural decisions.

• Strong proficiency in Python and SQL, including maintainable programming, complex queries, data transformation, and troubleshooting.

• Experience with pipeline testing, scheduling or orchestration, logging, data-quality validation, and failure investigation.

• Practical experience with at least one cloud or distributed-data platform, such as Databricks, Microsoft Azure, AWS, or Google Cloud.

• Strong understanding of probability, sampling, statistical inference, regression, confidence and prediction intervals, and uncertainty.

• Experience developing and evaluating statistical or machine learning models using appropriate validation methods and interpretation techniques.

• Familiarity with Git, code reviews, software testing, technical documentation, and reproducible analytical workflows.

• Ability to explain technical designs, analytical findings, assumptions, and limitations to both technical and non-technical audiences.

Preferred qualifications:

• Experience with Databricks, Spark/PySpark, or comparable distributed-data technologies.

• Knowledge of dimensional modeling, data warehouse or lakehouse architecture, 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 machine learning frameworks and tools such as XGBoost, LightGBM, PyMC, PyTorch, or TensorFlow.

• Experience working with utility datasets, distributed energy resource (DER) registries, load forecasting, sensor analytics, or engineering automation.

• Master's degree in a relevant quantitative or technical discipline.

Benefits:

• Comprehensive health and wellness coverage, including medical, dental, and vision insurance, with 100% of employee premiums covered.

• Generous paid time off.

• Paid holidays.

• Employee ownership opportunities through a share-based employee program.

• 401(k) retirement plan with an employer match of up to 4%, fully vested from day one.

• Opportunities to collaborate with recognized industry experts and energy-sector professionals.

• Remote work within the United States.

• Occasional business travel, expected to be 10% or less.

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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