Data Architecture & Data Engineering

The Data Architect defines and governs the target-state architecture for AdvanSix’s Unified Data Layer, ensuring that data across SAP, OT, lab, logistics, HSE, finance, and commercial domains is structured, integrated, and modeled for scale, trust, and reuse. This role owns the architectural blueprints, canonical data models, semantic design standards, integration patterns, and data product design principles that guide engineering delivery across the enterprise. The Data Architect partners closely with the AI, Automation, and Data team to ensure that what gets built is coherent, secure, and business-ready.Enterprise Data ArchitectureDefine and maintain the target-state architecture for the Unified Data Layer, including landing, curated, and semantic layers, domain boundaries, and consumption patterns for BI, APIs, AI, and automation.Develop architecture principles for modularity, interoperability, reusability, and governance across business and operational data domains.Serve as the design authority for major data platform and integration decisions.Canonical Data ModelingDesign enterprise and domain-level canonical models for key entities such as material, product, asset, equipment, work order, batch, vendor, customer, cost center, logistics shipment, quality event, and energy usage.Define standards for dimensional modeling, semantic modeling, event models, and reference data structures that support Power BI, AI, and operational decision-making.Ensure alignment of curated and semantic data products with master data strategy and KPI canon.Integration and Data Product DesignDefine architectural patterns for ingesting and harmonizing data from SAP S4 HANA, SAP DataSphere, historians, LIMS, TMS, HSE systems, and other enterprise platforms into the Unified Data Layer.Establish standards for data contracts, schema evolution, surrogate keys, versioning, partitioning, and semantic publication.Ensure that certified datasets and APIs are designed for downstream use by Reporting and BI, machine learning, Power Platform, and Copilot agents.Architecture Governance and Design ReviewLead design reviews for new data products, major source integrations, and semantic layer changes.Approve architectural patterns and ensure alignment with security, lineage, quality, retention, and access standards.Partner with Data Strategy and AI Governance to operationalize architecture guardrails into delivery standards.Collaboration with Engineering and OTWork with the OT Data Integration Lead to ensure plant data models, asset hierarchies, event frames, and OT schemas fit the enterprise architecture without taking over OT connectivity operations.Work with Big Data Engineers to translate architecture standards into implementable pipeline, storage, and semantic design patterns.Partner with the Manager, Unified Data Platform and AI Engineering on platform roadmap, architecture decisions, and technical debt prioritization.Documentation and EnablementProduce architecture diagrams, conceptual and logical data models, reference patterns, and design standards.Help stakeholders understand how data should be structured and consumed across the enterprise.Basic Qualifications:Minimum 8 years' of experience in enterprise data architecture, data modeling, and large-scale data platform design.Proven experience designing lakehouse or enterprise data platform architectures across multiple domains.Strong expertise in conceptual, logical, and physical data modeling, dimensional modeling, semantic layer design, and canonical data structures.Strong understanding of Azure-based data architectures, including Databricks or Fabric and Synapse, ADLS or OneLake, Data Factory or pipelines, and integration with Power BI.Working knowledge of SAP S4 HANA data structures and SAP DataSphere semantic concepts.Familiarity with OT and industrial data patterns, including historians, event frames, asset hierarchies, time-series data, and ISA-based structures.Strong knowledge of data contracts, schema versioning, lineage, master data alignment, and architecture governance.Excellent communication skills with the ability to translate architecture into clear implementation guidance.Preferred QualificationsExperience in manufacturing, chemicals, process industries, or industrial operations.Familiarity with TMS, LIMS, HSE, maintenance, and finance data domains.Experience with lineage and catalog tooling, master data design, and KPI canon alignment.Exposure to API-enabled data product design for AI agents and automation.Lean or Six Sigma mindset and comfort working in cross-functional transformation programs.