Remote- und Homeoffice Jobs in santa-fe ∙ Seite 3

By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use.  I further attest that all information I submit in my employment application is true to the best of my knowledge.

Job Description

Job description

About the role:
The Data Engineering Engineer IV collaborates with a multidisciplinary Agile team to build high-quality data pipelines that drive analytic solutions. This role focuses on generating insights from connected data, enabling advanced data-driven decision-making capabilities.

The Future Begins Here: At Takeda, we are creating a future-ready organization that uses data and digital to meet the needs of patients, our people, and the planet. We need your help to make this happen. Join our Innovation Capability Center (ICC) in Mexico City, Mexico.

At Takeda’s ICC we Unite in Diversity 
Takeda is committed to creating an inclusive and collaborative workplace, where individuals are recognized for their backgrounds and abilities they bring to our company. We are continuously improving our collaborators’ journey in Takeda, and we welcome applications from all qualified candidates. Here, you will feel welcomed, respected, and valued as an important contributor to our diverse team. 

The Opportunity

As a Data Engineer, you will be responsible for building and maintaining data systems and constructing datasets that are optimized for analysis, supporting Business Intelligence requirements and downstream systems.

Responsibilities

• Contribute to the data engineering of existing and new IT Systems to design, to analyze and implement complex manufacturing data driven solutions, with an impact on the daily operation of our manufacturing processes and facilities. 
• Enable data analytics (process trending, process modelling, real-time analytics, and predictive analytics) in a GxP environment from an IT perspective for all GMS sites  
• Develop and maintain scalable data pipelines using AWS native technologies and Databricks to support increasing data sources, volumes, and complexity. 
• Drive identification of technical issues, problem-solving and escalating issues appropriately. 
• Ensure Digital Products and Platforms are built efficiently, scalable and on state-of-the-art technology. 
• Provides guidance to business partners and solves complex technical changes and implementations. 
• Follow company and departmental policies, procedures and ensure documentation according to the Takeda Quality Management Systems (QMS), Software Development Life Cycle and Project Life Cycle standards. 
• Collaborate with analytics and business teams to improve data models that enhance business intelligence tools and dashboards, fostering data-driven decision-making across the organization. 

Skills and Qualifications

• Bachelor’s Degree in Computer Science or in a Technical/Natural Science (with focus on IT) or equivalent, 
• 1  year of experience in data engineering, or a related field, 
• In-Depth knowledge of SQL and Relational Databases, 
• Strong expertise in data modeling, and modern database technologies (Databricks, Oracle, MongoDB), 
• Basic understanding of batch-based manufacturing processes, 
• Understanding of Shop-Floor systems (MES, ERP, LIMS, Historian, etc.), 
• Understanding of Agile/SCRUM methodologies, 
• Understanding of good engineering practices (DevSecOps, source-code versioning), 
• Excellent written and verbal communication skills, with the ability to collaborate effectively with cross-functional teams, 
• Fluency in English. 

Nice to Have

• Experience with one more of the following analytics applications (Biovia Discoverant, Simca Online, PAS-X Savvy, Siemens gPROMS, 
• Experience with working in a highly regulated industry (e.g. pharmaceutical - or chemical industry) 
• Experience with AWS (S3, EC2, Terraform), 
• Experience in Computer Systems Validation GAMP, 
• Understanding of Data Science workflows, including Machine Learning and / or Deep Learning. 

Locations

Worker Type

Worker Sub-Type

Time Type