1403 – Senior Manager, AI and Analytics Data Engineer

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Do you want to make a global impact on patient health? Join our client’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. Their 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 leads engineering efforts to advance AI and data science applications from POCs and prototypes to full production.

As a Senior Manager, AI and Analytics Data Engineer, you will be part of 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. You will lead development of data solutions to support their data science community and drive data-centric decision-making.

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

ROLE RESPONSIBILITIES

• Lead development of data engineering processes to support data scientists and analytics/AI solutions, ensuring data quality, reliability, and efficiency
• As a data engineering tech lead, enforce best practices, standards, and documentation to ensure consistency and scalability, and facilitate related trainings
• Provide strategic and technical input on the AI ecosystem including 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
• Train and guide junior developers on concepts such as data modeling, database architecture, data pipeline management, data ops and automation, tools, and best practices
• Stay updated with the latest advancements in data engineering technologies and tools and evaluate their applicability for improving their 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
• Demonstrate a proactive approach to identifying and resolving potential system issues
• 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

BASIC QUALIFICATIONS

• 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)
• 7+ 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
• Recognized by peers as an expert in data engineering with deep expertise in data modeling, data governance, and data pipeline management principles
• In-depth 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
• 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)
• Highly self-motivated to deliver both independently and with strong team collaboration
• Ability to creatively take on new challenges and work outside comfort zone
• Strong English communication skills (written & verbal)

PREFERRED QUALIFICATIONS

• Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems, or a related discipline (preferred, but not required)
• 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.
• Experience working effectively in a distributed remote team environment
• Hands on experience working in Agile teams, processes, and practices
• 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

Please apply by sending your CV and a motivational letter in English.

Work Location Assignment: Hybrid