Data Engineering Intern

About EnerSys

EnerSys is a global leader in stored energy solutions for industrial applications. We have over thirty manufacturing and assembly plants worldwide servicing over 10,000 customers in more than 100 countries. Worldwide headquarters are located in Reading, PA, USA with regional headquarters in Europe and Asia. We complement our extensive line of Motive Power and Energy Systems with a full range of integrated services and systems. With sales and service locations throughout the world, and over 100 years of battery experience, EnerSys is the power/full solution for stored DC power products.

Motive Power applications include industrial lift trucks and pallet jacks, rail equipment, mining equipment, and airline ground support equipment. Some of the motive power brands include Hawker, Ironclad, General Battery, and Fiamm. Wherever there is a need for motive power, EnerSys offers the perfect energy solution.

AI/ML Engineering Team

EnerSys is a global leader in stored energy solutions for industrial applications. The AI/ML Engineering team builds intelligent systems across energy management, asset health monitoring, digital twin simulation, and manufacturing optimization. Across all of these initiatives, robust and well-engineered data infrastructure is a foundational requirement. Data Engineering at EnerSys operates at the interface of data systems and AI/ML development, with direct responsibility for the quality, availability, and structure of data that feeds production models and research workflows.

This role is not a traditional reporting or analytics function. It requires both the technical rigor of a data engineer and the domain fluency of someone who understands machine learning systems — how models consume data, where pipeline design decisions affect model performance, and how data quality issues manifest downstream in production.

We offer a 6-month internship.

Position Summary

The Data Engineering Intern will design and build data infrastructure that supports active AI/ML projects across EnerSys's portfolio — including energy management systems, digital twin simulation, predictive maintenance, and manufacturing optimization. The intern will contribute to ingestion pipelines, feature engineering workflows, data quality frameworks, and analytical tooling, working in close collaboration with AI/ML engineers and researchers throughout. A strong and demonstrated understanding of machine learning concepts and workflows is required; the intern will be expected to make data design decisions with the downstream model lifecycle explicitly in mind.

Essential Duties and Responsibilities

• Design and implement data ingestion, transformation, and validation pipelines for structured, semi-structured, and time-series data originating from BESS telemetry, industrial chargers, sensor networks, and manufacturing systems.
• Develop data quality and monitoring frameworks — including schema validation, completeness checks, outlier flagging, and drift detection — for both ML training pipelines and real-time inference inputs.
• Build and maintain feature engineering pipelines in collaboration with AI/ML engineers, with attention to temporal feature construction, normalization strategies, and feature store design.
• Design data models and storage schemas optimized for time-series retrieval, ML consumption, and cross-project reuse; evaluate tradeoffs across storage formats and database architectures including analytical, vector, and graph database systems.
• Contribute to streaming data pipeline development, supporting real-time ingestion and event-driven architectures for high-throughput industrial data sources.
• Contribute to synthetic data generation and simulation data workflows supporting the digital twin platform and EMS development.
• Develop exploratory analysis and visualization outputs that provide interpretable views of data quality, coverage, and distributional properties for research and project teams.
• Engage in the full engineering rigor expected of production AI/ML data systems — including pipeline testing and validation, data contract verification, simulation and