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Enterprise Architect - Information Management
Job in
Town of Belgium, Belgium, Ozaukee County, Wisconsin, 53004, USA
Listed on 2026-07-19
Listing for:
Atlas Copco
Full Time
position Listed on 2026-07-19
Job specializations:
-
IT/Tech
Data Engineering, Data Warehousing
Job Description & How to Apply Below
The role
The Enterprise Architect - Information Management plays a key role in enterprise-wide data and information architecture, ensuring that the cross-business area organization’s data landscape is coherent, secure, high quality and usable for advanced analytics and AI adoption. This role contributes to and evolves the long-term data strategy, drives architectural standards, and ensures that data becomes a strategic asset powering business value, automation, and AI-enabled decision making.
In alignment with Atlas Copco’s decentralized operating model, this role focuses on building collaborative relationships and delivering results through partnerships.
- A friendly, family-like atmosphere with opportunities to develop and grow
- A culture known for respectful interaction, ethical behavior and integrity
- An organization that uses diversity as a driver for performance
- Potential to see your ideas realized and make an impact
- New challenges and new things to learn every day
- Access to global career opportunities, as part of the Atlas Copco Group
- Develop and maintain the enterprise data architecture blueprint, covering different data domains, data products, flows, integrations, and lifecycle management.
- Enable the creation of information architecture standards, metadata, taxonomy, ontology, and master data structures.
- Facilitate consistency of data models across applications, platforms, and business units.
- Stimulate the design of data APIs, data products, and integration patterns.
- Foster implementation of data governance frameworks, including ownership clarity, stewardship, and data quality processes.
- Design for architecture that enables AI/ML, including feature stores, vector databases, model registries, and ML-Ops pipelines.
- Drive discussions cross the business areas to ensure data availability, quality, and governance.
- Promote ethical, transparent, and responsible data and AI practices.
- Support and drive modernization initiatives such as instrumentation, data platform consolidation, and legacy system rationalization.
- Conduct architectural assessments, gap analyses, and technology evaluations for data use cases.
- Contribute architectural guidance and oversight for programs and investments in information management.
- Work closely with business-IT leaders to translate strategic needs into data and AI capabilities.
- Partner with data engineers, data scientists, product teams, and IT operations to ensure successful delivery.
- Facilitate information management topics in architectural advisory boards and decision-making forums.
- Support innovation initiatives, proofs of concept, and emerging technology exploration.
- Develop a comprehensive view of the Group IT data landscape by identifying core data objects, analyzing their usage across systems and business areas, and establishing transparency on ownership and consumption.
- Relevant experience in information architecture, enterprise architecture, or data management roles.
- Master’s degree in computer science, information systems, data science, or equivalent experience.
- Strong background in data modeling, integration patterns, and API-driven architectures.
- Strong understanding of ETL/ELT, data pipelines, streaming technologies, and event-driven architectures.
- Experience with AI/ML platforms, ML-Ops, and analytics ecosystems.
- Knowledge of data governance tools, metadata management, and data cataloging.
- Effective conceptualization and design-thinking skills.
- Ability to present to management stakeholders and other business domains.
- Data-modeling and information classification at the enterprise level.
- Understanding of meta models, taxonomies and ontologies, as well as of the challenges of applying structured techniques (data modeling) to less-structured sources.
- Knowledge of data architectures, data ingestion techniques, data storage, MDM, BI, and data warehouse design and implementation techniques.
- Knowledge of problem analysis, structured analysis and design, and programming techniques.
- Ability to analyze project, program and portfolio needs, as well as to determine the resources needed to achieve objectives and overcome…
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