# Manager, Data Engineering & Semantics

**Company**: Pfizer
**Location**: Shanghai - Shanghai
**Experience**: senior
**Job type**: full-time
**Category**: Data Science
**Industry**: Healthcare

**Apply**: https://pfizer.wd1.myworkdayjobs.com/en-US/PfizerCareers/job/China---Shanghai---Shanghai/Manager--Data-Engineering---Semantics_4967779?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/job/manager-data-engineering-and-semantics-0c5534d2-f05/

## Description

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.

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## Skills

### Required
- data engineering
- data architecture
- data products
- reporting
- business intelligence
- semantic modeling
- metadata
- taxonomy
- ontology
- knowledge data engineering
- unstructured data engineering
- data enablement
- AI retrieval
- agents
- technical data governance
- data quality
- lineage
- observability
- China data privacy
- cross-border controls

### Nice to have
- pharmaceutical industry
- highly regulated industry
- China technology
- data privacy
- vendor environment

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Source: [Apply at pfizer.wd1.myworkdayjobs.com](https://pfizer.wd1.myworkdayjobs.com/en-US/PfizerCareers/job/China---Shanghai---Shanghai/Manager--Data-Engineering---Semantics_4967779?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)