REQUIRED QUALIFICATIONS:

• Must be a U.S. Citizen with the ability to obtain and maintain a U.S. DoD Secret Clearance.
• Bachelor’s degree in Computer Science, Software/Computer/Electrical Engineering, or a related field.
• 3+ years of experience in software development, data architecture, or related field.
• Proficiency in SQL, NoSQL and graph databases and their query languages (e.g. Neo4j, MongoDB, ArangoDB, GraphQL, Cypher).
• Proficiency in programming languages like Python, Java, or Scala.
• Experience working with tools and methods to structure and transform data into useful information - knowledge graphs, ontologies, semantic-web, etc.
• Experience with design and maintenance of data models.
• Experience building systems utilizing simultaneous data streams from real-world equipment and simulations such as Digital Twins.
• Excellent problem-solving skills and attention to detail.
• Excellent communication and teamwork skills to collaborate effectively across departments.
• Experience in conducting research and applying findings to practical applications.
• Desired Qualifications:
• Master’s or Ph.D. degree in Computer Science, Electrical Engineering, or a related field.
• Experience with machine learning, physics-informed deep learning, and deep learning frameworks such as TensorFlow or PyTorch.
• Familiarity with cloud platforms and services (e.g., AWS, Azure, Google Cloud).
• Experience with model-based design of data models, architectures, or ontologies.
• Experience with enterprise and manufacturing data systems, including PLM, ERP, and QMS systems.
• Experience working in an advanced manufacturing environment or developing tools and applications for manufacturing processes,
• Experience developing software products in a defense environment.
• Familiarity with machine control systems including sensing, communication, and system level controls for electric plants, machinery, and electromechanical systems (PLCs, SCADA, and OT systems).
• Experience with version control systems (e.g., Git, PLM, etc.).
• Proven track record of research contributions in relevant fields.
• An understanding of the DoD’s mission, capability gaps, and the ability to pursue new advanced manufacturing research projects to address these needs.
  If you meet the qualifications listed above and align with Integer’s Core Values, we encourage you to apply. Join our dynamic research and development team and contribute to the future of manufacturing excellence

• Design and Develop Data Architectures: Lead the development of data architectures and methods to integrate data into manufacturing applications, ensuring data integrity, accessibility, and scalability.
• Build and Maintain Scalable Data Pipelines: Design, develop, and maintain scalable data pipelines for extracting, transforming, and loading (ETL) data from various sources, including APIs, external data providers, and data streams from test equipment, sensors, and industrial control devices.
• Develop Methods for Data Ingestion: Develop methods for ingesting data from multiple sources, including product design information, code and software architecture, transactional data, and streaming data, enabling the creation of a unified view of data.
• Develop Applications for Data Availability and Validation: Develop applications to support data availability, validation, and transformation, ensuring that data is accurate, complete, and accessible to stakeholders.
• Lead Development of Cohesive Data Models: Lead the development of cohesive data models to connect and contextualize information from models, simulations, users, and manufacturing process data, enabling better decision-making and insights.
• Contribute to Manufacturing Design and Development: Contribute to the design of a holistic manufacturing design, development, and operations environment, serving as the subject matter expert for data architectures and analytics.
• Support Research Projects: Support active and future research projects by leading the development of data architectures and methods, ensuring that data engineering solutions are aligned with business objectives.
• Collaborate with Cross-Functional Teams: Collaborate with cross-functional teams, including data scientists, engineers, and manufacturing specialists, to provide technical expertise and support, ensuring that data engineering solutions meet business needs.
• Develop Automated Workflows: Develop automated workflows to streamline data operations and reduce manual intervention, improving efficiency and reducing errors.
• Maintain Database Infrastructure: Develop and manage the database infrastructure required to implement digital threads, ensuring that data is properly stored, managed, and secured.
• Maintain Clear Documentation: Maintain clear documentation for data systems, pipelines, and processes, ensuring that knowledge is shared across teams and that issues can be quickly resolved.