Why Patients Need You

All over the world, Pfizer colleagues work together to positively impact health for everyone, everywhere. Our colleagues have the opportunity to grow and develop a career that offers both individual and company success; be part of an ownership culture that values diversity and where all colleagues are energized and engaged and have the ability toimpact health and lives of millions of people. Digital is at the core of how Pfizer delivers Breakthroughs That Change Patient’s Lives. Advanced technologies that accelerate research, development, manufacturing, and patient access to therapies are all made possible by the infrastructures that enable our Digital landscape.

What You Will Achieve

The Infrastructure & Automation organization delivers excellence in the pursuit of those breakthroughs through industry-leading service performance. We ensure optimal performance of the network and hosting services that power Pfizer's business processes. We strive to revolutionize service dependability by applying advanced analytics to drive predictive detection, identifying potential issues with our services, and intervening before they disrupt our business. We place data at the heart of what we do and apply a relentless focus on continuous improvement to enable Pfizer’s business processes and patient outcomes.

ROLE SUMMARY

This role focuses on building, optimizing, and maintaining data pipelines, semantic data layers, and graph-based data models that support anomaly detection, incident prediction, event correlation, impact analysis, and automated knowledge generation. You will work extensively with digital IT service data, CMDB records, monitoring feeds, and infrastructure data from platforms like ServiceNow and cloud environments. Responsibilities include owning ingestion, processing, lineage, quality, and governance of complex IT operations data at scale—while shaping the semantic and ontological foundations that make infrastructure knowledge discoverable and machine-interpretable. The position requires strong engineering discipline, hands-on development ability, and comfort working with distributed systems and cross-functional teams.

ROLE RESPONSIBILITIES 

• Integrate and link data across monitoring tools, compliance and audit platforms, CMDB, documentation and knowledge systems to create a unified, trustworthy data ecosystem. 

  
  
  

• Build and maintain scalable data pipelines that ingest, process and transform infrastructure data from ServiceNow, monitoring systems, CMDB sources and cloud platforms. 

  
  
  

• Engineer Integrate and link data across monitoring tools, compliance and audit platforms, CMDB and knowledge systems to create a unified, trustworthy data ecosystem. 

  
  
  

• Build and maintain scalable data pipelines that ingest, process and transform infrastructure data from ServiceNow, monitoring systems, CMDB sources and cloud platforms. 

  
  
  

• Engineer data models to support ML-driven use cases such as anomaly detection, intelligent incident classification, dependency mapping and forecasting. 

  
  
  

• Implement data quality checks, validation frameworks, and anomaly detection mechanisms to ensure trustworthy analytics input. 

  
  
  

• Partner with AI/ML engineers and SRE teams to identify feature requirements, define data contracts and prepare training and inference datasets. 

  
  
  

• Collaborate with Data Engineers, platform SMEs, and analytics leads to align on semantic definitions across configuration items, assets and event data, while developing and governing semantic data layers that enforce consistent taxonomies, conceptual mappings, and standardized definitions across all datasets. 

  
  
  

• Optimize ETL/ELT jobs for cost performance and reliability across Azure, AWS, and on-prem platforms. 

  
  
  

• Maintain data lineage, metadata, and documentation to support governance, auditability, and downstream adoption. 

  
  
  

• Develop curated data products for PowerBI, predictive analytics and operational dashboards. 

  
  
  

• Lead data standardization efforts where possible and setup metrics that measure data quality. 

  
  
  

• Support platform integration initiatives by ensuring consistent data availability, versioning, and cross-system interoperabilitythrough semantic enrichment, entity resolution, and ontology-aligned metadata..

  
  
  

• Implement ontology-driven models and use graph databases (e.g., Neo4j, Neptune, Cosmos DB Graph API) to power relationship based analytics and improve data discoverability. 

  
  
  

• Define and maintain domain ontologies aligned with IT operations, infrastructure, ITSM and configuration management data structures. 

  
  
  

BASIC QUALIFICATIONS  

• Bachelor's degree in data engineering, computer science, or related field.

  
  
  

• 5+ years of strong multi-discipline data experience with experience in data concepts and technologies. 

  
  
  

• Building and maintaining ETL/ELT pipelines for structured and unstructured data. 

  
  
  

• Proficiency in Python and SQL for developing scalable data workflows. 

  
  
  

• Expertise in Big Data tools (Spark, Hadoop, Databricks) and streaming frameworks (Kafka). 

  
  
  

• Implementing semantic metadata and linking data to business glossaries for AI-readiness. 

  
  
  

• Hands-on experience with data modeling, schema design, and event-driven architecture. 

  
  
  

• Skilled in SQL, NoSQL, and vector databases for AI workflows. 

  
  
  

• Managing data lakes, warehouses, and cloud storage solutions (Azure Data Factory, Synapse, AWS Glue, Redshift, S3). 

  
  
  

• Designing frameworks for data validation, anomaly detection, and lineage tracking. 

  
  
  

• Implementing observability for AI pipelines and operational datasets (incidents, changes, monitoring metrics). 

  
  
  

• Strong understanding of APIs, micro services integration, and workflow automation. 

  
  
  

• Ability to work with ML engineers to operationalize training data pipelines and inference-ready datasets. 

  
  
  

• Strong communication skills to translate data engineering outputs into actionable insights. 

  
  
  

PREFERRED QUALIFICATIONS 

• Experience integrating with ServiceNow CMDB, ITSM, and operational data models. 

  
  
  

• Experience with Dataiku or similar platforms 

  
  
  

• Exposure to AIOps ecosystems (Dynatrace, Splunk, NetIM, Datadog). 

  
  
  

• Knowledge of graph databases and relationship modeling for dependency mapping. 

  
  
  

• Experience working with semantic technologies or enterprise ontologies. 

  
  
  

• Understanding ITIL, infrastructure performance metrics, and operational processes. 

  
  
  

NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS

Standard work schedule 

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.