Overview

THE ROLE

 

The Position: The Data Architect designs and governs the enterprise data architecture underpinning Great Dane's Durable Hub — the company's central data and integration hub — and leads the IT Data Engineering team that builds and operates it. This role owns the canonical data model, pipeline topology, and integration patterns that move data from source systems (JDE E1, Salesforce, Tacton CPQ, Concept, PLM, and other SaaS solutions) into the medallion (Bronze/Silver/Gold) architecture. The Data Architect partners with the Enterprise Architect, the Data Governance Committee, and Data engineering teams to ensure data is modeled once, governed centrally, and consumed reliably across analytics and operational use cases. The Data Architect also ensures Great Dane's enterprise data architecture is optimized for AI, machine learning, agentic workflows, and decision intelligence — designing data products, semantic models, the enterprise ontology, metadata structures, lineage, and knowledge representations that enable reliable consumption by analytics, predictive models, large language models (LLMs), and intelligent automation solutions.

 

Responsibilities

WHAT YOU’LL DO

• Lead and manage the IT Data Engineering team, including hiring, coaching, performance management, and workload prioritization, guiding engineers on state management, topic design, and data contract patterns.
• Own and evolve the Canonical Data Model (CDM) and enterprise object model — including the enterprise ontology that gives AI systems a shared, machine-interpretable definition of core business entities and their relationships — ensuring a single governed definition across the enterprise.
• Design end-to-end data pipeline topology for the central data and integration hub, including ingestion lanes (CDC, API polling, order-saga orchestration), stream processing (Kafka/Flink), and the Bronze/Silver/Gold medallion layers.
• Define integration patterns and specifications for source systems including JDE BSSV operations, Salesforce, and Tacton CPQ.
• Establish and maintain data modeling standards, event schemas, and immutability/replay ability principles to ensure data consistency, integrity, and reliability across analytics and operational systems
• Design and govern data architecture patterns that support advanced analytics, machine learning, generative AI, and agentic automation use cases.
• Define enterprise semantic models, the enterprise ontology, metadata standards, business glossaries, and knowledge representations that provide consistent context and interpretation for analytics, AI assistants, and intelligent agents.
• Establish standards for reusable data products that can be consumed by reporting, predictive models, digital assistants, and operational automation workflows.
• Partner with the AI Architect and Data Governance Committee to define lineage, explainability, auditability, privacy, and trust requirements for data used by AI solutions.
• Define architecture patterns and governance standards for structured, semi-structured, and unstructured data assets to support analytics, digital thread initiatives, and AI-enabled knowledge discovery.
• Partner with the Data Governance Committee to translate governance policy into enforceable technical architecture.
• Maintain architectural decision logs, marking superseded decisions rather than deleting them, and document final records to SharePoint and diagrams to Miro.
• Other duties as assigned.

Qualifications

YOUR SKILLS & ABILITIES (REQUIRED QUALIFICATIONS)

• Education: Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field; Master's preferred.
• Experience: 7+ years in data architecture, data engineering, or a related discipline, including hands-on design of enterprise data platforms and canonical/dimensional data models, and prior experience leading or managing a technical team. Demonstrated experience with streaming/event-driven architectures (Kafka/Confluent), lakehouse/medallion patterns, and integration with ERP and CRM systems.
• Skills: Proven ability to lead, mentor, and develop a data engineering team.
  • Deep proficiency in data modeling, SQL, and stream processing (Flink or equivalent); familiarity with dbt, Iceberg, and cloud data platforms (AWS). AWS certification a plus.
  • Modern Data Architecture: Experience designing data products, semantic layers, and enterprise metadata architectures.
  • AI Data Foundations: Understanding of data architectures supporting machine learning, RAG (Retrieval Augmented Generation), vector search, ontologies (including the CDM) and knowledge graphs, and AI-enabled business applications.
  • Data Governance & Trust: Knowledge of data lineage, data quality, ability to explain, and governance requirements for AI systems.
  • Strong grasp of data governance concepts and canonical modeling. Excellent written communication and stakeholder-facing documentation.
• Travel: Occasional; typically less than 10%.

PHYSICAL/MENTAL REQUIREMENTS:

• Office and plant environment.
• Keyboarding, lifting, standing, bending, walking.
• Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

BENEFITS OVERVIEW

• Competitive compensation
• Benefits, including but not limited to dental, vision, and medical with employer contributions
• Retirement programs, including a Pension Plan and 401(k) Plan with employer match
• Tuition Reimbursement
• Paid holidays and vacation
• And more!