Director, Ai and Analytics Data Engineering Lead

Role SummaryAs 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.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- 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 workQualificationsMust-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.Work Location Assignment: HybridEEO (Equal Employment Opportunity) & Employment EligibilityPfizer is committed to equal opportunity in the terms and conditi