Lead Data Engineering, Integration & Governance

USTBC ABB Installation Products Inc.; Lombardia; Hybrid; EUR 90,000 - 120,000; Full time

Job summary

ABB is seeking a Lead Data Engineering, Integration & Governance to build and operate the data engineering foundation for our Digital Engineering Data Strategy. You will own ingestion, preparation, transformation, quality assurance, and integration across PLM, ERP, MES, and other tools, enabling downstream analytics, digital twin platforms, and AAS-based solutions.

The role emphasizes governance, lifecycle management, and collaboration with stakeholders to scale Digital Engineering CoE standards

Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, Information Systems, or related field.
• Proven experience designing and implementing data pipelines and reusable data products across multiple enterprise systems.
• Experience with data quality, metadata management, lifecycle management, governance, and automation.
• Practical experience with modern data engineering platforms and pipeline frameworks (Databricks, Azure Data Factory, or similar).

Responsibilities

• Lead the ingestion, preparation, transformation, quality assurance, and integration of data from enterprise source systems.
• Design and evolve scalable data engineering architectures and integration patterns for enterprise interoperability.
• Drive data quality, lineage, and governance, ensuring data is trusted and usable across platforms.
• Oversee operations, governance, security, and lifecycle controls within data platforms.
• Collaborate with stakeholders to implement end-to-end data integration and governance standards.

Skills

Data pipelines

ETL/ELT

Data integration

Data governance

Communication

Education

BSc/MSc in CS/SE/IS or related

Tools

Databricks

Azure Data Factory

Job description

At ABB , we help industries run leaner and cleaner-and every person here makes that happen. You'll be empowered to lead, supported to grow, and proud of the impact we create together. Join us and help run what runs the world.

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

You will be mainly accountable for:

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 metadata management, data catalogs/registries, data lineage, and traceability. Exposure to semantic integration, knowledge graphs, and ontology-driven architectures is a plus
• Excellent communication skills-able to bridge the gap between technical teams and business stakeholders. Strong problem-solving and decision-making abilities, with