Senior Data Engineer & AI Platform Lead (Hybrid)

Takeda Pharmaceutical Co.; Ciudad de México; Híbrido; MXN 1.800.000 - 2.400.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive Salary + Performance Bonus

Comprehensive Healthcare Plans

Flexible Working Schemes

Hybrid work model

Diversity and Inclusion Programs

Descripción de la vacante

Takeda Pharmaceutical Co. in Mexico City is seeking a senior data engineer to join the Innovation Capability Center. You will build data models, data pipelines, and reusable components that enable AI-native data solutions for life sciences data sources.

You will collaborate with product managers, data architects, and ML engineers to deliver high-quality data products, apply best practices, and mentor junior SDEs in a hybrid work setting.

Formación

• 12+ years building data products as a Data Engineer or similar field.
• 12+ years working in Python and SQL with a data focus.
• Experience leading projects and managing timelines.
• Experience managing multiple customer-focused implementation projects.
• Experience with data plotting/dashboarding tools like React or Streamlit.

Responsabilidades

• Build data models, prototypes, and integration solutions for customers.
• Develop Python libraries and reusable parsing functions for pre-clinical data.
• Create high-quality data pipelines with unit and integration tests.
• Develop data apps, reports, and dashboards using React, Streamlit, and Jupyter.
• Collaborate with product, project, and data teams to deliver high-impact data products.
• Lead process and tech improvements to improve quality and developer experience.

Conocimientos

Data engineering

Python

SQL

Data modeling

Leadership

Agile

DevOps

Dashboarding

Herramientas

React

Streamlit

Jupyter

Descripción del empleo

Takeda Pharmaceutical Co. in Mexico City is seeking a senior data engineer to join the Innovation Capability Center. You will build data models, data pipelines, and reusable components that enable AI-native data solutions for life sciences data sources.

You will collaborate with product managers, data architects, and ML engineers to deliver high-quality data products, apply best practices, and mentor junior SDEs in a hybrid work setting.