Role Summary

Do you want to make a global impact on patient health? Join Pfizer Digital’s Artificial Intelligence, Data, and Advanced Analytics organization (AIDA) to leverage cutting-edge technology for critical business decisions and enhance customer experiences for colleagues, patients, and physicians. Our team is at the forefront of Pfizer’s transformation into a digitally driven organization, using data science and AI to change patients’ lives. The Data Science Industrialization team is a key driver of Pfizer’s digital transformation, leading engineering efforts to advance AI and data science applications from prototypes and MVPs to full production.

As the AI and Analytics Data Engineering Lead, you will lead a global team responsible for designing, developing, and implementing robust data layers that support data scientists and key advanced analytics/AI/ML business solutions. You will partner with cross-functional data scientists and Digital leaders to ensure efficient and reliable data flow across the organization. Your expertise in data engineering will support our data science community and drive data-centric decision-making.

Join us in making an impact on patient health through the application of cutting-edge technology and collaboration with a diverse team.

Role Responsibilities

• Provide leadership, supervision, and mentorship for a global team of analytics data engineers
• Lead development of data engineering processes to support data scientists and analytics/AI solutions, ensuring data quality, reliability, and efficiency
• Establish and enforce data engineering best practices, standards, and documentation to ensure consistency and scalability, and facilitate related trainings
• Partner with Data Science Industrialization leaders to define team roadmap and provide strategic and technical input on platform evolution, vendor scan, and new capability development
• Act as a subject matter expert for data engineering on cross functional teams in bespoke organizational initiatives by providing thought leadership and execution support for data engineering needs
• Stay updated with the latest advancements in data engineering technologies and tools and evaluate their applicability for improving our data engineering capabilities
• Direct data engineering research to advance design and development capabilities
• Collaborate with stakeholders to understand data requirements and address them with data solutions
• Partner with the AIDA Data and Platforms teams to enforce best practices for data engineering and data solutions
• Communicate the value of reusable data components to end-user functions (e.g., Commercial, Research and Development, and Global Supply) and promote innovative, scalable data engineering approaches to accelerate data science and AI work

Qualifications

Must-Have

• 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).
• 10+ 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.
• 2-3 years of hands-on experience leading data engineering, data science, or ML engineering teams
• Track record of managing stakeholder groups and effecting change
• Recognized by peers as an expert in data engineering with deep expertise in data modeling, data governance, and data pipeline management principles
• Expert knowledge of modern data engineering frameworks and tools such as Snowflake, Redshift, Spark, Airflow, Hadoop, Kafka, and related technologies
• Experience working in a cloud-based analytics ecosystem (AWS, Snowflake, etc.)
• Familiarity with machine learning and AI technologies and their integration with data engineering pipelines
• Excellent communication skills to clearly articulate expectations, capabilities, and action plans; actively listen and share information with the team; influence without direct authority
• Expertise in leading end-to-end projects by translating business priorities and vision into product/platform thinking, breaking down complex initiatives into action plans, providing functional and technical guidance and SME support, and transitioning to support processes
• Fosters a strong team by sharing responsibility, providing guidance, and developing team members through frequent communication and teamwork.
• Demonstrated experience interfacing with internal and external teams to develop innovative data solutions
• Strong understanding of Software Development Life Cycle (SDLC) and data science development lifecycle (CRISP)
• Hands on experience working in Agile teams, processes, and practices
• Ability to creatively take on new challenges and work outside comfort zone.
• Strong English communication skills (written & verbal)

Nice-to-Have

• Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems, or a related discipline (preferred, but not required)
• Experience in solution architecture & design
• Experience in software/product engineering
• Experience with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker or other data science platforms
• Familiarity with containerization technologies like Docker and orchestration platforms like Kubernetes.
• Expertise in cloud platforms such as AWS, Azure or GCP.
• Proficiency in using version control systems like Git.
• Pharma & Life Science commercial functional knowledge
• Pharma & Life Science commercial data literacy
• Experience working effectively in a distributed remote team environment.

EEO (Equal Employment Opportunity) & Employment Eligibility 

Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, or disability.