Manager, Data Engineer – AI and Automation

As a Manager, Data Engineer – AI and Automation at Pfizer, you will lead data modeling and engineering initiatives while overseeing the development of test plans and scripts. You'll collaborate with teams to enhance data quality, design AI models for complex analytics, and manage data platforms that facilitate digital transformations in healthcare. Your role also involves providing technical guidance and documents to stakeholders, ensuring successful project outcomes.

To be considered for the Manager, Data Engineer – AI and Automation role at Pfizer, you should possess a bachelor’s or master’s degree in computer science or a related field. You will need over four years of experience in data engineering or architecture and at least two years related to AI and machine learning. Proficiency in programming languages such as Python and SQL is essential, alongside experience with data-driven tools and platforms.

The Manager, Data Engineer – AI and Automation plays a crucial role in enhancing patient outcomes at Pfizer by leveraging advanced analytics and AI models to optimize data-driven decision-making. By ensuring high-quality data management and implementing innovative solutions, you contribute directly to the research and development of life-saving medications and treatments.

In the Manager, Data Engineer – AI and Automation position at Pfizer, familiarity with various technologies is crucial. You should have experience with cloud platforms like AWS or Azure, big data technologies such as Hadoop, and ETL tools like Apache NiFi. Competence with AI-driven tools for automating data pipelines and knowledge of containerization tools like Docker and Kubernetes will also be invaluable.

A career as a Manager, Data Engineer – AI and Automation at Pfizer can lead to senior leadership positions or specialized roles within data science and engineering. With a focus on innovation and patient care, you can advance into roles that drive larger data initiatives across the organization, or move into consultant positions where you guide other teams in data strategy and implementation.

When answering this question, focus on specific projects where you've successfully implemented AI or machine learning solutions. Explain the problem you aimed to solve, the methods you employed, and the impact of your work on the organization or patients. Providing quantifiable results will strengthen your response.

Discuss your approach towards data validation, cleaning, and preprocessing. Highlight your knowledge of tools and methodologies, like establishing robust ETL processes, conducting thorough testing, and implementing best practices for data governance to maintain data integrity.

Mention specific tools you've worked with, like Apache Airflow for workflow automation or Snowflake for data warehousing. Discuss why you prefer them and how they have enhanced your efficiency in managing large datasets.

Share your strategies for continuous learning such as subscribing to industry journals, attending conferences, or participating in online courses. Your enthusiasm for self-improvement demonstrates a proactive attitude important for this role.

Choose a specific example that showcases your problem-solving skills. Describe the challenge, your analytical approach, the solution you implemented, and the positive results achieved. This will help interviewers understand your thought process and expertise.

Explain your communication tactics, including regular check-ins, collaborative tools (like JIRA or Slack), and fostering an environment where team members feel valued and encouraged to share ideas. Highlight experiences where effective collaboration led to successful project outcomes.

Be prepared to discuss different data modeling techniques, such as entity-relationship models, dimensional modeling, or normalized data schemas. Provide examples of how you have applied these techniques in your previous roles to create effective data architectures.

Detail a systematic approach to task prioritization, such as assessing project deadlines, the impact of completion, and resource availability. Stress the importance of communication with stakeholders to manage expectations effectively.

Provide specifics on cloud services you've utilized, such as AWS or Google Cloud. Discuss any projects that involved deploying solutions in these environments and how it improved performance or scalability.

Convey your passion for healthcare and how data engineering can significantly influence patient care. Highlight alignment between Pfizer's values and your career goals, and express enthusiasm for contributing to innovative solutions transforming the pharmaceuticals landscape.