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The global Information Technology (IT) Function is leading efforts to align IT and Business Strategy, leverage IT investments, and optimize end to end business processes and associated information integration technologies. Through these efforts, IT helps to improve the competitive position of Corning's businesses through IT enabled processes. IT also delivers Information Technology applications, infrastructure, and project services in a cost efficient manner to Corning worldwide.

Role Summary: The Data Architect is responsible for the design and evolution of our enterprise data architecture, with a strong focus on reusable data pipelines, data schemas & products, and modern data engineering practices. This role requires an AI-first mindset in data engineering and data modeling activities that enable the business to use data effectively and consistently across domains.

You will partner closely with product owners, data engineers, data analysts, and business stakeholders to:

Define and maintain enterprise data models and domain boundaries using traditional and AI-enabled methods
Design and govern reusable data assets and data products
Establish standards and patterns for robust, scalable data pipelines
Ensure data is discoverable, trustworthy, and fit-for-purpose for analytics, ML, and operational use cases

The ideal candidate blends deep technical expertise with strong business acumen and can translate complex business needs into clear, implementable data designs. They will evaluate and recommend different solutions and provide leadership to the product and engineering teams in delivering architecture, design, and development of solutions across multiple data products.

Essential Responsibilities:

Domain Data Architecture

Define and evolve conceptual, logical, and physical data models across core business domains.
Establish canonical and semantic models, shared dimensions, and reference data to enable consistent reporting and cross-functional integration.
Design reusable business entities, metrics, and data structures that support enterprise-wide consistency and scalability.
Partner with business stakeholders to translate processes, KPIs, and analytical needs into effective data models and semantic layers.
Recommend fit-for-purpose modeling approaches based on business and technical requirements.

Data Products and Reusable Design Patterns

Define standards for data products, including structure, naming, documentation, versioning, quality expectations, and lifecycle management.
Develop reusable patterns for data ingestion, transformation, and consumption that can be scaled across teams and use cases.
Partner with data engineering teams to create reference architectures, templates, and reusable implementation assets.
Establish schema design and evolution practices that support ongoing change while minimizing downstream impact.
Contribute to iterative solution delivery through Agile and DevOps practices.

Data Pipeline and Platform Architecture

Design end-to-end data pipeline architectures for batch, near real time, and event-driven use cases.
Define logical and physical architecture patterns using cloud-native platforms and modern data technologies.
Guide the development of scalable, secure, reliable, and cost-effective data solutions using tools such as Databricks, dbt, SQL, Python or Scala, and orchestration frameworks.
Ensure solutions are built with strong observability, testing, performance, and operational reliability.
Collaborate with engineering teams to support production-ready implementation and continuous improvement.

Governance, Standards, and Technical Leadership

Embed data governance, security, compliance, lineage, metadata, and retention requirements into architecture and design decisions.
Define and promote standards for data quality, validation, cataloging, access controls, and engineering best practices.
Provide technical leadership and mentoring to data engineers, analysts, and architects to drive consistent adoption of standards and patterns.
Evaluate emerging technologies and architectural approaches and make recommendations aligned to business priorities.

Qualifications/Requirements:

7–10 years of relevant IT experience, with at least 4+ years in a Data Architect, Senior Data Engineer, or similar role focused on data design and modeling.
5+ years of experience designing and implementing data models for analytics and operational reporting (dimensional, normalized, and/or domain-driven designs).
5+ years of experience building and/or guiding the development of ETL/ELT pipelines in cloud or modern data platforms.
4+ years of hands-on experience with Databricks or similar cloud-based lakehouse platforms supporting enterprise data lakes, data warehouses, and business intelligence
2+ years of experience using dbt (or similar transformation frameworks) including model structuring, tests, and documentation.
Strong proficiency in SQL and experience with at least one programming language used in data engineering (Python or Scala preferred).
Experience using Git and related DevOps tooling (GitHub/GitLab/Bitbucket, CI/CD pipelines such as GitHub Actions, Azure DevOps, Jenkins, etc.).
Experience with data catalog/lineage/metadata tools is a plus (e.g., Collibra, Alation, Unity Catalog, Purview).
Familiarity with modern data architecture paradigms such as data mesh and domain-oriented architecture

Desired Qualifications:

The successful candidate for this position will have the following general experience and expertise:

Ability to navigate ambiguity, make decisions with incomplete information, and drive consensus.
Strong problem-solving skills with a structured, analytical approach.
Highly self-motivated, organized, and able to manage multiple priorities.
Experience working with distributed and/or international teams is a plus.
Strong communication and presentation skills, with the ability to engage technical and non-technical audiences effectively

Travel : Limited work travel

IT Data Architect

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