Information Technology Director

About Graphic

At Graphic Packaging International, we produce the paper cup that held your coffee this morning, the basket that transported those bottles of craft beer you enjoyed last weekend, and the microwave tray that heated your gourmet meal last night. We’re one of the largest manufacturers of paperboard and paper-based packaging for some of the world’s most recognized brands of food, beverage, foodservice, household, personal care and pet products. Headquartered in Atlanta, Georgia, we are collaborative, diverse, innovative individuals who create inspired packaging while giving back to our communities.

With over 25,000 employees working in more than 130 locations worldwide, we strive to be environmentally responsible in our industry and in the communities where we operate. We are committed to workplace diversity and offer compensation and benefits programs that are among the industry’s best to reward the talented people who make our company successful.

If this sounds like something you would like to be a part of, we’d love to hear from you.

A World of Difference. Made Possible.

JOB SUMMARY:

Reporting to the VP, Data & AI, the Director, Data Platform & AI Engineering will lead Graphic Packaging’s data platform and AI engineering function, building and operating the governed data and AI infrastructure that the enterprise’s data strategy depends on. As the senior technical leader for this function, the Director will own the architecture, delivery, and operational rigor behind the data lakehouse, the AI and agent layer built on top of it, and the boundary between the two.

The Director will translate the enterprise’s data-first strategy into working infrastructure: a unified lakehouse foundation, a defined AI control plane, and a delivery model built to scale from pilot to production without compromising governance. This leader will manage a mix of internal engineers, contracted resources, and vendor relationships while building the internal technical capability GPI needs to reduce dependence on external delivery partners over time.

The Director serves as the technical strategist within GPI’s data and AI organization, translating governance and use-case priorities into platform capability and bridging data platform delivery with AI product and fusion teams so infrastructure supports both near-term use cases and long-term enterprise AI capability. The Director is accountable for establishing data contracts, DataGovOps practices, and platform standards that enable safe, auditable AI deployments across manufacturing, supply chain, and finance domains, partnering closely with the AI Governance and Data Governance leads on policy and use-case alignment, and with Finance and manufacturing operations leadership on cost discipline and OT integration, respectively.

ESSENTIAL DUTIES & RESPONSIBILITIES:

Data & AI Platform Strategy and Architecture:

• Establish and own the technical architecture for GPI’s governed data platform that does not exist today and must be designed and implemented from the ground up.
• Define and evolve the AI and agent architecture, including the ontology/semantic layer.
• Establish and maintain the boundary between data architecture and AI architecture, and between data engineering and AI data engineering, so ownership stays unambiguous as the team grows.
• Partner with the VP, Data & AI on platform decisions (Databricks, Microsoft, Palantir, or a combination) and own the technical execution once decisions are made.

Platform Engineering and Operations:

• Build and operate the data pipelines, ingestion, and integration patterns that consolidate GPI’s fragmented data estate.
• Own platform reliability, scalability, and cost, including execution of a fixed annual compute budget with workloads tagged to the business function consuming them.
• Lead the technical evaluation and onboarding of the Databricks POC, and support the Palantir evaluation for

AI Engineering, Agents, and the Control Plane:

• Own the AI Control Plane: models, prompts and skills, agents, tools and MCP, context and semantics, policies and guardrails, identity, human approval, and observability/FinOps.
• Apply full governance to AI built on GPI’s platform, and boundary governance (identity, policies and guardrails, observability and cost) to AI embedded in tools GPI already owns.
• Assess and resolve the production-readiness of AI agents built outside formal governance today, including security model, integration pattern, and the sourcing decision for the contracted engineers who built them.
• In partnership with Enterprise Architecture and Cybersecurity, design and operate agentic AI capabilities across Personal, Operational, and Enterprise use cases, each governed to its own risk weight rather than a single one-size rule.

Data Governance & Product Management:

• Operationalize governance-as-code: establish machine-readable data contracts, metadata standards, and dynamic validation checks within Unity Catalog to enforce policy without manual review, embedding governance into the pipeline rather than a parallel process.
• Define data product ownership: partner with the Data Governance Lead and business data stewards to clarify data product definitions, ownership, and SLAs so business units can confidently consume governed data assets for AI and analytics.
• Lead DataGovOps practices: automate lineage tracking, access-control auditing, and data quality monitoring via active metadata and observability tooling.
• Cross-functional collaboration: partner with the AI Governance lead, the Data Governance Lead, and business domain experts to align platform capability with AI use-case requirements and enterprise risk posture.

Team Leadership and Delivery:

• Hire, lead, and develop a team spanning data architecture, data engineering, and AI architecture, insourcing delivery capability that today sits with contracted and third-party resources.
• Manage delivery partners and contracted resources, with a clear plan to convert or replace contract capacity with permanent hires where it makes sense.
• Partner with the AI Governance function, a peer function within this organization, to ensure AI Committee priorities translate into engineering delivery, without collapsing the distinction between AI governance and AI technical work.
• Build toward GPI’s target operating model, governed self-service for the business, by defining what a business unit can configure on shared platforms versus what remains centrally engineered.

REQUIRED TECHNICAL COMPETENCIES:

• Data lakehouse architecture & open table formats: converged architecture design, open table formats (Delta Lake, Iceberg, Hudi), schema management, and performance optimization across BI, data science, and AI workloads.
• AI platform and MLOps integration: model versioning, reproducible training pipelines, RAG data preparation, and monitoring deployed models in production.
• Cloud data and AI resource management: cost optimization (FinOps) and scaling compute for data and AI workloads; familiarity with GPU infrastructure, inference cost, and hybrid/multi-cloud deployment.
• Data security, compliance, and lineage: role-based and fine-grained access control (Unity Catalog policies), regulatory compliance, and audit-ready data lineage for sensitive data across both plant operations and back-office systems (finance, HR, commercial records).
• Metadata management and observability: data catalogs, lineage tools, and observability platforms that enable self-service discovery, SLA monitoring, and AI use-case impact analysis.
• Manufacturing data integration: PLM, MES, SCM, and IoT/sensor data streams, and the ability to architect interoperable ingestion across IT and OT systems.
• Enterprise back-office integration: ERP finance and HR modules, CRM, and shared-services systems, held to the same governance and quality bar as plant-floor data.

CANDIDATE PROFILE:

The successful candidate will bring the following experience and expertise:

• Bachelor’s degree in computer science, data engineering, information systems, or a related discipline; advanced degree a plus.
• 10+ years of progressive data engineering, data architecture, or platform engineering experience, including experience architecting a governed data lakehouse (Databricks, Snowflake, or equivalent) at enterprise scale.
• Hands-on experience with AI/ML infrastructure (embedding pipelines, vector stores, feature stores, RAG retrieval) sufficient to architect and evaluate AI engineering work directly, not just data engineering.
• Experience distinguishing and structuring data engineering from AI engineering disciplines, and translating that structure into team design and delivery ownership.
• Experience managing a blended team of employees and contracted or vendor resources, including converting contract capacity to internal capability over time.
• Manufacturing, CPG, or back-office/shared-services experience a plus; experience working across a fragmented, multi-ERP data estate, plant and back-office systems alike, strongly preferred.
• Demonstrated ability to build foundational infrastructure while resisting pressure to skip governance for speed.
• Experience evaluating or implementing Palantir Foundry, or a comparable ontology/semantic layer platform, a plus.

COMPETENCIES:

• Data & AI Architecture: designs and defends a governed platform architecture end to end.
• Delivery & Vendor Management: manages contracted resources and vendor relationships toward measurable outcomes.
• Governance Judgment: knows where to hold the line and where to flex, consistent with a “firm but not fixed” governance model.
• Cross-Functional Influence: works effectively with AI Governance, Data Governance, and business stakeholders without direct authority over all of them.
• Technical Credibility: hands-on enough to evaluate architecture decisions personally, not just manage those who make them.
• Team Building & Capability Development: builds a high-performing blended team of engineers, contractors, and vendor partners; establishes upskilling paths in emerging disciplines (prompt engineering, RAG, governance-as-code).
• Change Management: communicates the value of governed data and AI platforms to diverse stakeholders and drives adoption of new practices and tooling.

SUCCESS MEASURES:

• Reduction in pilot-to-production cycle time for new data and AI use cases.
• Growth in data assets with clear ownership, access controls, and SLAs in Unity Catalog.
• Number of data engineers upskilled in DataGovOps and AI engineering practices.
• Adoption: number of teams consuming governed data products; AI use cases deployed with platform-enforced governance.
• Platform uptime and SLA adherence across the lakehouse, Unity Catalog, and AI inference layers.
• Reduction in reliance on external delivery partners, with internal capability maturity over 12 to 24 months.

Disclaimer

The candidate must be able to perform the essential functions of the position satisfactorily, with or without a reasonable accommodation. Graphic Packaging retains the right to change or assign other duties to this position.

Pay Range

$156,600.00 - $208,800.00

GPI Benefits Program

Competitive Pay

401(k) w/employer matching

Health & Welfare Benefits

Medical, dental, vision, and prescription drug coverage

Short and Long-Term Disability

Life Insurance

Accidental Death & Dismemberment (AD&D) Insurance

Flexible Spending and Health Savings Accounts

Various Voluntary benefits

Adoption Assistance Program

Employee Discount Programs

Employee Assistance Program

Tuition Assistance Program

Paid Time Off + paid company holidays each year

Applications accepted on an ongoing basis and there is no deadline.

Recruitment Agency Partnership Notice

We value partnerships and maintain a strict process for engaging external agencies. Due to compliance requirements, we do not accept unsolicited resumes. Agencies must be fully onboarded and have an active, executed agreement in place with Graphic Packaging to submit candidates for open positions. Any unsolicited resumes will be considered the property of Graphic Packaging, and no recruitment fees will be owed.

Requisition: 16761