Senior Associate, Data Engineer – AI and Automation
Pfizer's purpose is to deliver breakthroughs that change patients' lives.
We are seeking a highly skilled and motivated AI Engineer to join our advanced technology team.
Role Responsibilities:
* Develop and implement artificial intelligence models and algorithms to drive innovation and efficiency in our Data Analytics and Supply Chain solutions.
* Create test plans, test scripts, and perform data validation.
* Design and implement Cloud Data Lake, Data Warehouse, Data Marts, and Data APIs.
* Develop complex data products beneficial for PGS and allow for reusability across the enterprise.
* Collaborate with contractors to deliver technical enhancements.
* Develop automated systems for building, testing, monitoring, and deploying ETL data pipelines within a continuous integration environment.
* Develop internal APIs and data solutions to enhance application functionality and facilitate connectivity.
* Analyze data, enhancing its quality and consistency.
* Conduct root cause analysis and address production data issues.
* 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.
Basic Qualifications:
* Bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, or a related discipline.
* Over 2 years of experience as a Data Engineer, Data Architect, or in Data Warehousing, Data Modeling, and Data Transformations.
* Over 1 year 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.
* Strong understanding of data structures, algorithms, and software design principles.
* Programming Languages: Experience in Python, SQL, and familiarity with Java or Scala.
* Big Data Technologies: Familiarity with Hadoop, Spark, and Kafka for big data processing.
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.
* 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.
* 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.
* 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