LEAD DATA ENGINEERING, INTEGRATION

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Denna position rapporterar till:

Center of Excellence Lead Digital Engineering

Your role and responsibilities :

The Lead Data Engineering, Integration & Governance is responsible for building and operating the data engineering foundation for the Digital Engineering Data Strategy. This role leads the ingestion, preparation, transformation, quality assurance, and integration of data from enterprise source systems (PLM, ERP, MES, engineering tools, simulation platforms, product configuration systems). The role ensures data is technically available, trusted, traceable, and ready for consumption by semantic integration layers, knowledge graphs, metadata/catalog capabilities, Digital Twin platforms, Asset Administration Shell (AAS), sustainability platforms, analytics applications, and downstream systems. This role bridges hands-on data engineering, enterprise integration, operational excellence, and governance implementation to enable a scalable, data-driven Digital Engineering organization.

The work model for the role is: #LI- Hybrid

Data Engineering & Integration

• Design and evolve scalable data engineering architectures, integration patterns, and database structures that enable ingestion and interoperability across enterprise systems.
• Build and operate robust data integration capabilities, including ETL/ELT pipelines, workflows, APIs, automation frameworks, and reusable data products that support semantic platforms, Digital Twins, AAS, analytics, and other downstream consumers.

Data Preparation, Transformation & Quality

• Lead the preparation, transformation, harmonization, enrichment, and semantic mapping of enterprise data for knowledge graph population, registry/catalog publication, and downstream consumption. Establish data quality, validation, traceability, lineage, monitoring, exception-handling, and version-control practices to ensure data accuracy, consistency, usability, and controlled evolution across platforms.

Operations, Governance, Compliance & Lifecycle Management

• Drive operational excellence for data platforms by managing pipeline operations, integration workflows, quality reporting, issue resolution, migration activities, platform upgrades, and continuous improvement initiatives.
• Implement governance, compliance, security, and lifecycle controls by embedding policies, metadata management, catalog integration, access management, auditing, and regulatory requirements into data products and workflows.

Technical Oversight & Infrastructure

• Optimize and maintain enterprise data platforms through infrastructure consolidation, performance tuning, availability and disaster recovery planning, workspace management, automation, monitoring, root-cause analysis, and technology evaluation.

Semantic Enablement & Downstream Consumption

• Prepare and expose semantically ready data that supports ontologies, knowledge graphs, semantic integration layers, and canonical information structures required for enterprise interoperability.
• Enable and support delivery of structured outputs, transformations, and integration mechanisms for Digital Twin platforms, Asset Administration Shell (AAS), Omniverse, sustainability solutions, analytics applications, and other consumer-specific services.

Collaboration & Stakeholder Engagement

• Partner with business stakeholders, architects, semantic modelers, domain experts, vendors, and platform teams to implement end-to-end data integration, maintain technical documentation, and drive scaling of Digital Engineering CoE standards and best practices.

Qualifications for the role :

• Bachelor's or Master's degree in Computer Science, Software Engineering, Information Systems, or related field
• Proven experience working with large-scale data in complex enterprise environments. Strong track record designing and implementing data pipelines, ingestion frameworks, transformation logic, and reusable data products across multiple enterprise systems
• Experience supporting data quality, metadata management, lifecycle management, operational processes, and technical governance implementation. Strong hands-on experience with data engineering, ETL/ELT pipelines, data ingestion, transformation, preparation, and quality automation
• Practical experience with modern data engineering platforms and pipeline frameworks (Databricks, Azure Data Factory, or similar orchestration and transformation tools)
• Experience with met