Manager, AI and Analytics Data Engineer

ROLE RESPONSIBILITIES

1. Develop data solutions to support data scientists and analytics/AI solutions, ensuring data quality, reliability, and efficiency
2. Conduct exploratory data analysis and quality checks
3. Deliver scalable data pipelines that ingest and integrate data from various information sources
4. Contribute to best practices, standards, and documentation to ensure consistency and scalability
5. Conduct data engineering research to advance design and development capabilities
6. Guide junior developers on concepts such as data modeling, database architecture, data pipeline management, data ops and automation, tools, and best practices
7. Demonstrate a proactive approach to identifying and resolving potential system issues
8. Create and maintain robust technical documentation for data solutions to enable knowledge retention and sharing
9. Collaborate with data scientists, engineers, and colleagues from across Pfizer to integrate AI and data science models into production solutions
10. Partner with the AIDA Data and Platforms teams to enforce best practices for data engineering and data solutions

BASIC QUALIFICATIONS

1. Bachelor's degree in computer science, information technology, software engineering, or a related field (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering, or a related discipline).
2. 5+ years of hands-on experience in working with SQL, Python, object-oriented scripting languages (e.g. Java, C++, etc..) in building data pipelines and processes. Proficiency in SQL programming, including the ability to create and debug stored procedures, functions, and views.
3. Knowledge of modern data engineering frameworks and tools such as Snowflake, Redshift, Spark, Airflow, Hadoop, Kafka, and related technologies
4. Experience working in a cloud-based analytics ecosystem (AWS, Snowflake, etc.)
5. Understanding of Software Development Life Cycle (SDLC) and data science development lifecycle (CRISP)
6. Highly self-motivated to deliver both independently and with strong team collaboration
7. Ability to creatively take on new challenges and work outside comfort zone.
8. Strong English communication skills (written & verbal)

PREFERRED QUALIFICATIONS

1. Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems, or a related discipline (preferred, but not required)
2. Experience with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker or other data science platforms
3. Familiarity with machine learning and AI technologies and their integration with data engineering pipelines
4. Familiarity with containerization technologies like Docker and orchestration platforms like Kubernetes.
5. Experience working effectively in a distributed remote team environment
6. Hands on experience working in Agile teams, processes, and practices
7. Proficiency in using version control systems like Git.
8. Pharma & Life Science commercial functional knowledge
9. Pharma & Life Science commercial data literacy