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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Manager, Data Warehouse Platform & Operations Engineer - **Company:** Pfizer Inc. - **Location:** New York, NY, United States (Remote available) - **Experience:** Expert - **Salary:** $99,200.0 - $165,400.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Data Analysis, ARM Architecture, Configuration Management, Databases, Data Infrastructure, Data Integration, Data Security, Data Sharing, Data Warehousing, Graph Database, Laboratory Information Management Systems, Metadata, Operational Data Store, Cloud Services, DataOps, SQL Databases, Computer Network Operations, Snowflake, Generative AI, Agentic-AI, Data Layers, Data Lakes, Collibra, Process Control Systems, Real Time Data, Model Context Protocol, GXP - **Published:** October 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/manager-data-warehouse-platform-operations-engineer-new-york-ny--a98d0422-414a-4a2a-b991-55f51411b5be ## About the Role Data Warehousing, Metadata, Drug Discovery, Manufacturing, Configuration Management, Analysis Skills, Database Architecture, Database Management Software/Systems (DBMS), Database Administration, Systems Administration/Management, * Applicant must have a bachelor's degree with at least 4 years of experience; OR a master's degree with at least 2 years of experience; OR a PhD with 0+ years of experience; OR as associate's degree with 8 years of experience; OR a high school diploma (or equivalent) and 10 years of relevant experience. * Demonstrated experience supporting platform or product roadmaps, driving adoption, introducing new capabilities, and translating business priorities into scalable technology outcomes. * Experience defining or managing data products, common data models, consumption layers, or shared data services for multiple business and technology consumer groups. * Strong working knowledge of modern data warehousing, SQL, cloud data platforms, data modeling, data integration, performance, security, governance, and DataOps concepts sufficient to guide decisions and evaluate solution designs. * Experience with Snowflake or a comparable modern cloud data platform and familiarity with analytics and AI consumption patterns. * Demonstrated ability to influence without direct authority and communicate platform priorities, trade-offs, measures, and outcomes to business, Enterprise Analytics, Digital, architecture, and engineering stakeholders. Preferred Qualifications * Advanced degree in a relevant discipline. * Experience with Snowflake capabilities such as Snowpark, Streams, Tasks, Dynamic Tables, secure data sharing, and Snowflake Cortex. * Experience with AI-ready data products, AI agents, MCP, Retrieval-Augmented Generation (RAG), semantic layers, ontologies, knowledge graphs, or governed AI-consumption patterns. * Experience developing platform-adoption, data-product-health, DataOps, cost, value-realization, or AI-readiness scorecards and dashboards. * Experience with metadata, catalog, lineage, data quality, observability, and governed-access capabilities, including tools such as Collibra and Acceldata. * Knowledge of pharmaceutical manufacturing, supply chain, quality, GxP, or other regulated data environments. * Experience operating in a federated model that combines business ownership with Digital engineering and platform delivery. PHYSICAL/MENTAL REQUIREMENTS Ability to perform complex analysis and exercise sound judgment across interdependent business, data, platform, architecture, financial, and technology considerations. Ability to evaluate detailed roadmaps, solution designs, adoption measures, and scorecards where requirements or priorities may be incomplete or competing. NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS ## Description The Data Platform & Operations Engineer will drive adoption, value realization, and continued expansion of the Pfizer Global Supply Operations focused on Manufacturing & Network Operations. The role will coordinate delivery of new capabilities, support continuous modernization and innovation, and help establish PGS OPS as a trusted foundation for operational analytics, artificial intelligence (AI), and reusable manufacturing and quality data products. The incumbent will partner with Digital Manufacturing, Site teams, and Enterprise Analytics to onboard high-value use cases and operational data sources. The role requires broad manufacturing-domain knowledge and an understanding of how near real-time data is collected from shop-floor and quality-laboratory systems, contextualized, governed, and integrated into Snowflake-based data lake and consumption environments. Strong working knowledge of data warehousing, Structured Query Language (SQL), Snowflake, Cortex capabilities, AI agents, and Model Context Protocol (MCP) is required to guide decisions and assess solution designs, without serving as the primary hands-on engineering delivery role. Role Responsibilities * Drive adoption and value realization of the PGS OPS platform across Manufacturing and Quality by identifying priority sites, users, data products, and operational use cases. * Develop and execute adoption plans that expand platform utilization, self-service access, and reuse of operational data products across sites and functional teams. * Support continuous modernization of PGS OPS by identifying capability gaps, assessing emerging operational-data and AI capabilities, recommending priorities, and partnering with Digital to deliver approved enhancements. * Partner with Manufacturing, Sites, Enterprise Analytics, and Digital teams to define and deliver reusable operational data products aligned to common manufacturing and quality concepts and business outcomes. * Apply manufacturing-domain knowledge across production execution, process monitoring, asset performance, batch operations, quality control, laboratory operations, and operational excellence to shape useful data products. * Maintain a broad understanding of near real-time shop-floor and laboratory integration patterns, including how data from manufacturing execution systems, process control systems, historians, equipment, and laboratory information management systems is extracted, contextualized, transformed, reconciled, governed, and made available in PGS OPS. * Coordinate onboarding of new manufacturing sites, source systems, and data domains while promoting consistent metadata, lineage, data quality, security, and reusable integration patterns. * Guide responsible use of Snowflake and relevant Cortex capabilities, including practical standards for AI agents and MCP solutions, while maintaining sufficient technical depth to assess designs and alignment with platform direction. * Develop and automate scorecards measuring platform adoption, data-product health, consumption, operational performance, business value, and AI readiness; use insights to support prioritization and continuous improvement.