Manager, Data Engineer - AI and Automation

ROLE SUMMARY
Pfizer is seeking a highly skilled and motivated AI Engineer to join our advanced technology team. The successful candidate will be responsible for developing, implementing, and optimizing artificial intelligence models and algorithms to drive innovation and efficiency in our Data Analytics and Supply Chain solutions. This role demands a collaborative mindset, a passion for cutting-edge technology, and a commitment to improving patient outcomes.
ROLE RESPONSIBILITIES

• Lead data modeling and engineering efforts within advanced data platforms teams to achieve digital outcomes.
• Provides guidance and may lead/co-lead moderately complex projects.
• Oversee the development and execution of test plans, creation of test scripts, and thorough data validation processes.
• Lead the architecture, design, and implementation of Cloud Data Lake, Data Warehouse, Data Marts, and Data APIs.

• Lead the development of complex data products that benefit PGS and ensure reusability across the enterprise.
• Collaborate effectively with contractors to deliver technical enhancements.
• Oversee the development of automated systems for building, testing, monitoring, and deploying ETL data pipelines within a continuous integration environment.

• Collaborate with backend engineering teams to analyze data, enhancing its quality and consistency.

• Conduct root cause analysis and address production data issues.

• Lead the design, develop, and implement AI models and algorithms to solve sophisticated data analytics and supply chain initiatives.

• Stay abreast of the latest advancements in AI and machine learning technologies and apply them to Pfizer's projects.
• Provide technical expertise and guidance to team members and stakeholders on AI-related initiatives.
• Document and present findings, methodologies, and project outcomes to various stakeholders.
• Integrate and collaborate with different technical teams across Digital to drive overall implementation and delivery.
• Ability to work with large and complex datasets, including data cleaning, preprocessing, and feature selection.

BASIC QUALIFICATIONS

• A bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, or a related discipline.
• 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
• Over 5 4+years of experience as a Data Engineer, Data Architect, or in Data Warehousing, Data Modeling, and Data Transformations.
• Over 2+ years of experience in AI, machine learning, and large language models (LLMs) development and deployment.
• Proven track record of successfully implementing AI solutions in a healthcare or pharmaceutical setting is preferred.
• Strong understanding of data structures, algorithms, and software design principles
• Programming Languages: Proficiency in Python, SQL, and familiarity with Java or Scala
• AI and Automation: Knowledge of AI-driven tools for data pipeline automation, such as Apache Airflow or Prefect. Ability to use GenAI or Agents to augment data engineering practices

PREFERRED QUALIFICATIONS

• Data Warehousing: Experience with data warehousing solutions such as Amazon Redshift, Google BigQuery, or Snowflake.
• ETL Tools: Knowledge of ETL tools like Apache NiFi, Talend, or Informatica.
• Big Data Technologies: Familiarity with Hadoop, Spark, and Kafka for big data processing.
• Cloud Platforms: Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
• Containerization: Understanding of Docker and Kubernetes for containerization and orchestration.
• Data Integration: Skills in integrating data from various sources, including APIs, databases, and external files.
• Data Modeling: Understanding of data modeling and database design principles, including graph technologies like Neo4j or Amazon Neptune.
• Structured Data: Proficiency in handling structured data from relational databases, data warehouses, and spreadsheets.
• Unstructured Data: Experience with unstructured data sources such as text, images, and log files, and tools like Apache Solr or Elasticsearch.
• Data Excellence: Familiarity with data excellence concepts, including data governance, data quality management, and data stewardship.

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