Requisition Number: 74882

The company built on breakthroughs.
Join us.

Corning is one of the world’s leading innovators in glass, ceramic, and materials science. From the depths of the ocean to the farthest reaches of space, our technologies push the boundaries of what’s possible.

How do we do this? With our people. They break through limitations and expectations – not once in a career, but every day. They help move our company, and the world, forward.

At Corning, there are endless possibilities for making an impact. You can help connect the unconnected, drive the future of automobiles, transform at-home entertainment, and ensure the delivery of lifesaving medicines. And so much more.

Come break through with us.

Our Optical Communications segment has recently evolved from being a manufacturer of optical fiber and cable, hardware and equipment to being a comprehensive provider of industry-leading optical solutions across the broader communications industry.This segment is classified into two main product groupings – carrier network and enterprise network. The carrier network product group consists primarily of products and solutions for optical-based communications infrastructure for services such as video, data and voice communications. The enterprise network product group consists primarily of optical-based communication networks sold to businesses, governments and individuals for their own use.

• Build and maintain data pipelines — Develop, test, and support reliable data pipelines that ingest, transform, and deliver manufacturing, operational, and business data for batch and near real-time use cases.
• Support data platform operations and continuous improvement — Help maintain data infrastructure and workflows to ensure data availability, performance, and reliability for reporting, analytics, downstream applications, and AI/ML use cases. Assist with troubleshooting data issues, improving pipeline efficiency, documenting data flows, and following engineering best practices for maintainable and scalable data solutions.
• Implement data quality processes — Apply automated checks, validation rules, and monitoring to help ensure the accuracy, completeness, and consistency of manufacturing and operational data, while supporting data readiness for AI-driven analytics and machine learning applications.
• Develop production-ready datasets — Work with manufacturing teams, analysts, data scientists, and other business partners to understand data needs and deliver clean, usable datasets that support reporting, analysis, operational decision-making, and AI/ML model development.
• Support AI/ML data integration — Prepare, transform, and structure manufacturing and operational data for AI/ML use cases, including enabling model training, feature development, inference workflows, and data availability for intelligent monitoring and automation solutions.

• Data Engineer SME Path — For individuals whose passion is deep technical mastery and subject‑matter expertise
• • Build strong expertise in data platforms, pipelines, and domain-specific data, with opportunities to become a go-to resource for specific systems or manufacturing data domains
  • Contribute to technical design discussions, code reviews, and documentation while continuing to strengthen engineering best practices
  • Grow influence by improving data engineering standards, solution patterns, and data quality practices across projects and teams, building the depth of technical expertise and ownership expected for progression into a Senior Data Engineer role
• ML/AI Jump Path – For individuals interested in accelerating towards advanced analytics, AI, and ML-driven leadership roles
• • Support the development of AI/ML-enabled data engineering solutions, including intelligent data quality, automated observability, and data workflows that enable AI-driven use cases
  • Work closely with the Decision Intelligence Group and other partners to support advanced analytics, ML, and AI use cases through reliable, well-structured data
  • Gain exposure and experience that prepares them for future technical growth within advanced analytics, AI/ML, or data engineering specialization roles
• Technical Managerial Path – For individuals motivated by people leadership and team development in a technical environment
• • Build leadership skills through mentoring peers, sharing knowledge, and helping coordinate work across projects and team efforts
  • Take on increasing responsibility in project delivery, stakeholder collaboration, and cross-functional coordination across IT and business teams

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related technical discipline
• 2+ years of experience in data engineering or related roles, with experience building, maintaining, and supporting ETL/ELT pipelines, data integration workflows, data warehouses, or cloud-based data platforms
• Experience working with manufacturing, operational, or industrial data, including integrating and transforming data that supports production, quality, equipment, or supply chain processes
• 2+ years of programming experience in Python, with the ability to write clean, maintainable, and testable code
• Experience supporting data solutions used for analytics, reporting, or AI/ML applications, including exposure to AI-enabled workflows, machine learning data preparation, or data pipelines that support intelligent automation
• Hands-on experience with data processing and integration technologies such as Apache Spark, SQL-based transformation tools, Kafka, or similar batch and streaming technologies
• Experience with cloud platforms such as AWS, GCP, or Azure, including familiarity with data storage, compute, and managed data services used in modern data architectures
• Proficiency in SQL, data modeling, and database technologies, including relational databases and modern data warehousing environments
• Strong communication and collaboration skills, with the ability to work effectively across cross-functional teams including manufacturing, analytics, IT, and business stakeholders

• Experience in manufacturing, industrial IoT, or scientific research environments, with exposure to time-series data, sensor data ingestion, and high-volume data processing from operational technology (OT) systems
• Proficiency or hands-on experience with the following tools:
  • Databases & Data Warehousing: Oracle, Microsoft SQL Server, PostreSQL
  • Enterprise ETL & Integration Platforms: Informatica, Mulesoft, SSIS
  • Open Source Data Integration & DAG Orchestration: Airflow, Dagster, Prefect
  • Manufacturing & Industrial Data Sources: Pi Integrator, Camstar, Maximo