Data Architect

🔥 0 minutes ago

⚔️ Virginia – Remote

⚔️ Virginia – Remote

💵 $118.8k - $178.2k / year

⏰ Full Time

🟠 Senior

🔴 Lead

🚰 Data Engineer

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AdvanSix

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2016

🏭 Manufacturing

🤝 B2B

💰 $12M Grant - AdvanSix on 2024-09

Manufacturing • B2B

AdvanSix is a U. S. -based chemical manufacturer that produces nylon 6, caprolactam, ammonium sulfate and other chemical intermediates and performance materials for industrial customers. The company operates integrated chemical production facilities supplying polymers, fibers and specialty chemicals to customers in plastics, textiles, agriculture (fertilizers) and other industrial markets. AdvanSix primarily serves business customers (B2B) and focuses on large-scale manufacturing and materials production.

📋 Description

• Define 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. • Design 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. • Define 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. • Lead 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. • Work 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. • Produce architecture diagrams, conceptual and logical data models, reference patterns, and design standards. • Create reusable templates and guidance for engineers, analysts, and vendors. • Help stakeholders understand how data should be structured and consumed across the enterprise.

🎯 Requirements

• 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.