Manager, Data Engineering & Semantics

The Manager, Data Engineering & Semantics is a delivery owner within Data Engineering & Semantics, responsible for hands-on data engineering, data product and data quality delivery.

Key Accountabilities:

• Manage day-to-day delivery for assigned data engineering, data product, semantic or data-quality outcomes.
• Own day-to-day delivery of Data Engineering across L1–L4 , ingestion, integration, transformation, storage and serving for structured, unstructured and knowledge data, and reporting and business intelligence foundations.
• Own day-to-day delivery of reusable, governed data products and data agents, including the CL layer of the AI Empowered Consumption Layer (AICL) and agent-facing data contracts.
• Own day-to-day delivery of technical semantics , semantic models, metadata, taxonomies and technical mappings that implement DSM-approved business definitions and harmonized KPIs.
• Own day-to-day delivery of Data Trust & Reliability Engineering , technical data governance controls, shift-left data quality checks, lineage, observability and China data, privacy and cross-border controls.
• Maintain clear backlog, dependencies, risks, acceptance criteria, test evidence, documentation and transition plans.
• Coordinate with GCA DSM on definitions and issue resolution, with Enterprise Engineering, PIC and partners on productionization, and coach Senior Associates.

Required Capabilities & Competencies:

• Modern data engineering, data architecture, data products and reporting and business intelligence foundations.
• Hands-on proficiency in one or more mainstream BI platforms (e.g. Microsoft Power BI, Tableau, Qlik Sense or SAP Analytics Cloud).
• Semantic modeling, metadata, taxonomy and ontology; knowledge and unstructured data engineering; data enablement for AI retrieval and agents.
• Technical data governance, data quality, lineage and observability, and China data privacy and cross-border controls.
• Outcome ownership: connects decisions to measurable business, user, service and transformation outcomes.
• AI-first and enterprise-minded: applies responsible AI, reuse, architecture, security, privacy and scale principles appropriate to the work.
• POD collaboration: enables clear accountability, effective cross-functional execution and minimal handoff friction across GCA DSM, AITO, Enterprise Engineering, PIC and CEI PODs.
• Precision and learning: communicates clearly, documents decisions, uses evidence and telemetry and continuously improves delivery.
• Bachelor’s degree or above in Computer Science, Data Engineering, Data Science, Information Systems or a related computing discipline.
• 8+ years of progressive relevant experience.
• Agile delivery, product metrics, adoption measurement and evidence-based iteration.
• Demonstrated ability to work across business, product, technology, data and governance stakeholders in a matrixed environment.

Measures of Success:

• Timely, high-quality delivery of governed, reusable data products, semantic models and the AICL CL layer.
• Consistent implementation of DSM-approved business definitions and harmonized KPIs across reporting, analytics and AI.
• Improved data quality, lineage, observability and adherence to China data and privacy requirements.
• Faster POD delivery through trusted, reusable and AI-ready data.