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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and operating data engineering foundations, including data ingestion, transformation, and quality assurance, while ensuring compliance and governance across enterprise data platforms. Proficient in implementing data integration capabilities and managing operational excellence for scalable, data-driven environments.
Highest-signal resume keywords
Data EngineeringETL/ELT PipelinesData Quality ManagementMetadata ManagementData Governance Implementation
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data IngestionData TransformationData PreparationData AutomationData LineageData CatalogsData ProductsData Quality AssuranceOperational ProcessesComplex Enterprise Environments
Soft Skills
Excellent CommunicationProblem-SolvingDecision-MakingInfluencing Without Authority
Tools & Technologies
DatabricksAzure Data FactoryData Integration FrameworksSemantic Integration LayersKnowledge Graphs
Certifications & Qualifications
Bachelor's Degree in Computer ScienceMaster's Degree in Software Engineering
Industry Keywords
Digital EngineeringData StrategyOperational ExcellenceGovernanceCompliance
Tech Stack
Tools & technologiesAzureERPETL
About the role
Key responsibilities & impact- 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.
- 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.
- 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.
- 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.
- 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.
- Prepare and expose semantically ready data that supports ontologies, knowledge graphs, semantic integration layers, and canonical information structures required for enterprise interoperability.
- 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.
Requirements
What you’ll need- 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 a hands-on and ownership-driven approach
- Ability to navigate complex organizational structures and influence without direct authority.
Benefits
Comp & perks- Flexible working hours
- Professional development opportunities