Data Engineering Intern

Navitas Systems LLC, a leader in comprehensive energy storage solutions, was formed in 2011 with the merger of MicroSun Innovative Energy Storage Solutions and MicroSun Electronics, and the acquisition of lithium battery company A123 Systems’ Government Solutions Group. In 2019, East Penn Manufacturing, one of the world’s leading battery manufacturers, acquired majority interest in Navitas Systems. In SEVEN remarkable decades, East Penn has grown from a one-room shop with a product line of five automotive batteries to one of the world’s leading battery manufacturers with over 10,500 full-time employees, 515 product designs, operations around the world, and hundreds of awards for industry excellence.

At our Navitas facilities we engage in the research, design, development, and manufacture of advanced lithium cells and energy storage products and systems for both commercial customers and U.S. Government/military customers. We invest in our people, they are and always will be the heart of this company. Are you ready to join a family-owned enterprise committed to honoring the contributions of everyone? Then Navitas is the right place for you.

We are seeking a curious and technically strong Data Engineering Intern to join our BIS (Business Intelligence & Systems) team. The intern will help build and maintain the data pipelines, integrations, and warehouse infrastructure that power analytics, reporting, and decision-making across Navitas.

This is a hands-on opportunity to work directly with enterprise systems and contribute to the foundation of a modern data platform being built from the ground up. The intern will gain real-world experience in SQL development, ETL/ELT design, data modeling, and integration patterns in a fast-paced manufacturing environment.

Working hours will be scheduled around the intern's class schedule, but must be the same days and hours each week. The intern is expected to confirm their weekly schedule with their manager within two weeks of their start date.

• Assist in designing, building, and maintaining ETL/ELT pipelines that move data from source systems (ERP, MES, CRM, SharePoint, Excel) into the central data warehouse.
• Write, optimize, and troubleshoot SQL queries, stored procedures, and views against SQL Server databases.
• Support the design and implementation of star schema models, data marts, and semantic layers that feed Power BI and downstream consumers.
• Help connect and integrate enterprise systems (Expandable ERP, Ignition MES, Salesforce, Dayforce, Jira) into the central data platform.
• Build and run validation checks to ensure accuracy, completeness, and consistency of data flowing through pipelines.
• Contribute to scheduled jobs, refresh cadences, and automation efforts that reduce manual data movement and reporting overhead.
• Assist in monitoring pipeline health, identifying failures, and resolving data issues in partnership with IT and BIS team members.
• Document data sources, transformations, schemas, and lineage; contribute to data governance and metric standardization efforts.
• Use Jira to manage work items and SharePoint to publish documentation; collaborate with BI developers, analysts, and business stakeholders.

Required

• Currently pursuing a Bachelor's degree in Computer Science, Data Engineering, Information Systems, Software Engineering, or a related field.
• Strong working knowledge of SQL (joins, aggregations, subqueries, window functions) against relational databases.
• Familiarity with SQL Server or comparable RDBMS (PostgreSQL, MySQL, Oracle).
• Understanding of ETL/ELT concepts, data warehousing fundamentals, and relational data modeling.
• Proficiency in at least one scripting language (Python preferred; PowerShell or similar acceptable).
• Familiarity with Microsoft SharePoint for collaboration and documentation.
• Familiarity with Jira (or similar work management tools) for tracking tasks and stories.

Preferred

• Exposure to Power BI (semantic models, Power Query, DAX basics).
• Familiarity with star schema / dimensional modeling (Kimball methodology).
• Experience with Git and version control workflows.
• Exposure to cloud data platforms (Azure, Fabric, OneLake, Databricks, Snowflake) or on-premises data lake/lakehouse concepts.
• Familiarity with APIs, REST, JSON, and integration patterns between SaaS systems.
• Exposure to MES, ERP, or manufacturing data environments.
• Understanding of data governance, lineage, and metadata management concepts.
• Coursework or projects involving data pipelines, distributed systems, or large-scale data processing.