Role Summary
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.
Role Responsibilities
- Provide leadership, supervision, and mentorship for a integral 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 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.
Work Location Assignment: Hybrid
EEO (Equal Employment Opportunity) & Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditi

📌 Director, Ai and Analytics Data Engineering Lead (Ciudad de México)
🏢 Pfizer
📍 Ciudad de México