The Senior Manager is a capability leader within Data Engineering & Semantics, owning one or more capability areas — such as Data Engineering, Data Product Management or Data Trust & Reliability Engineering — while retaining sufficient breadth to lead across the team mandate and complex CEI PODs. The role turns DSM-led business requirements into trusted, governed and AI-ready data, including the CL layer of the AI Empowered Consumption Layer (AICL), so that reporting, analytics and AI solutions work from one consistent data foundation.
Responsibilities
Own one or more team capability areas end to end, defining capability roadmaps, engineering standards, reusable patterns, controls, service expectations and lifecycle outcomes across China CEI
Lead and govern Data Engineering across L1–L4 — ingestion, integration, transformation, storage and serving for structured, unstructured and knowledge data, and reporting and business intelligence foundations
Lead technical management and lifecycle governance of BI platforms, including workspace and environment administration, access controls, release management, refresh reliability, performance, capacity and license utilization
Lead and govern Data Product Management — reusable, governed data products and data agents, including the CL layer of the AI Empowered Consumption Layer (AICL) and agent-facing data contracts
Lead and govern Technical Semantics — semantic models, metadata, taxonomies and technical mappings that implement DSM-approved business definitions and harmonized KPIs
Requirements
Bachelor’s degree or above in Computer Science, Data Engineering, Data Science, Information Systems or a related computing discipline
The Senior Manager is a capability leader within Data Engineering & Semantics, owning one or more capability areas — such as Data Engineering, Data Product Management or Data Trust & Reliability Engineering — while retaining sufficient breadth to lead across the team mandate and complex CEI PODs. The role turns DSM-led business requirements into trusted, governed and AI-ready data, including the CL layer of the AI Empowered Consumption Layer (AICL), so that reporting, analytics and AI solutions work from one consistent data foundation.
Responsibilities
Own one or more team capability areas end to end, defining capability roadmaps, engineering standards, reusable patterns, controls, service expectations and lifecycle outcomes across China CEI.
Lead and govern Data Engineering across L1–L4 — ingestion, integration, transformation, storage and serving for structured, unstructured and knowledge data, and reporting and business intelligence foundations.
Lead technical management and lifecycle governance of BI platforms, including workspace and environment administration, access controls, release management, refresh reliability, performance, capacity and license utilization; partner with Enterprise Engineering for production operations and implement GCA DSM-approved business definitions and KPIs.
Lead and govern Data Product Management — reusable, governed data products and data agents, including the CL layer of the AI Empowered Consumption Layer (AICL) and agent-facing data contracts.
Lead and govern Technical Semantics — semantic models, metadata, taxonomies and technical mappings that implement DSM-approved business definitions and harmonized KPIs.
The Manager is a delivery owner within Data Engineering & Semantics, and may be assigned to a specific capability or deployed into a cross-functional CEI POD based on portfolio priorities. The role combines hands-on data engineering, data product and data quality delivery with backlog, quality, readiness and stakeholder accountability, including delivery of the AICL CL layer.
Responsibilities
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.
Requirements
Bachelor’s degree or above in Computer Science, Data Engineering, Data Science, Information Systems or a related computing discipline
Lead and optimize maintenance strategies and processes for industrial equipment, ensuring reliability, cost efficiency, and safety across plant operations.
Responsibilities
Align maintenance strategies with plant performance, safety, reliability, cost and business-continuity objectives
Create, review and continuously improve preventive, predictive and condition-based maintenance plans using equipment criticality and failure history
Lead FMEA, risk analysis, breakdown analysis and root-cause problem solving, converting findings into robust maintenance, spare-parts, training or design actions
Govern SAP PM / CMMS master data, maintenance plans, task lists, work-order history and breakdown data quality
Monitor and improve maintenance KPIs, including breakdowns, BD/ton, MTBF, MTTR, downtime, PM compliance and PM effectiveness
Requirements
Bachelor’s Degree in Mechanical
Bachelor’s Degree in Electrical
Bachelor’s Degree in Mechatronics
Bachelor’s Degree in Industrial Engineering
Nice to have: digital and data-visualization skills