Job Title: Data Engineering Lead (mAI – Data Foundation Core) Location Preference:
2nd: Anywhere in Switzerland
3rd: Anywhere in the European Union
Experience:
12–15+ years in Data Engineering, Cloud Data Platforms, Data Architecture, and Enterprise Data Solutions.
Role Summary:
Lead the Data Foundation for AI platform by designing scalable AWS-based data architecture, building enterprise data pipelines, establishing governance, and enabling reusable data services for Insights Generator, Value Dossier, Pricing, OSS, and mAI Assistant.
Key Responsibilities
Lead the architecture and implementation of AWS-based structured and unstructured data platforms supporting AI and GenAI solutions.
Design scalable data ingestion pipelines, data models, metadata, lineage, data quality, and governance frameworks.
Establish reusable data contracts, integration patterns, and secure data access across enterprise AI products.
Collaborate with Takeda Enterprise Architecture, Data Engineering, AI, Security, and Product teams to define the Data Foundation roadmap.
Drive engineering standards, technical reviews, backlog prioritization, and mentor distributed engineering teams.
Required Skills
AWS services: S3, Glue, Lambda, Athena, Redshift, EMR, Lake Formation, Step Functions, Event Bridge, API Gateway, IAM
Python, PySpark, SQL
Data Lake/Lakehouse architecture
GenAI, RAG, Vector Databases, Knowledge Graphs
Terraform, GitHub, CI/CD
Excellent stakeholder management and communication
#J-18808-Ljbffr
2nd: Anywhere in Switzerland
3rd: Anywhere in the European Union
Experience:
12–15+ years in Data Engineering, Cloud Data Platforms, Data Architecture, and Enterprise Data Solutions.
Role Summary:
Lead the Data Foundation for AI platform by designing scalable AWS-based data architecture, building enterprise data pipelines, establishing governance, and enabling reusable data services for Insights Generator, Value Dossier, Pricing, OSS, and mAI Assistant.
Key Responsibilities
Lead the architecture and implementation of AWS-based structured and unstructured data platforms supporting AI and GenAI solutions.
Design scalable data ingestion pipelines, data models, metadata, lineage, data quality, and governance frameworks.
Establish reusable data contracts, integration patterns, and secure data access across enterprise AI products.
Collaborate with Takeda Enterprise Architecture, Data Engineering, AI, Security, and Product teams to define the Data Foundation roadmap.
Drive engineering standards, technical reviews, backlog prioritization, and mentor distributed engineering teams.
Required Skills
AWS services: S3, Glue, Lambda, Athena, Redshift, EMR, Lake Formation, Step Functions, Event Bridge, API Gateway, IAM
Python, PySpark, SQL
Data Lake/Lakehouse architecture
GenAI, RAG, Vector Databases, Knowledge Graphs
Terraform, GitHub, CI/CD
Excellent stakeholder management and communication
#J-18808-Ljbffr
