Platform Manager - AI & Data Platforms
Takeda · Bengaluru
Job Description
The Opportunity
As a Platform Manager for AI-Enabled Data Platforms, Architecture & FinOps, you will manage and evolve Takeda’s next-generation Data, AI, and platform capabilities. This role blends agentic AI enablement, enterprise solution and platform architecture, tokenomics/FinOps governance, and user-centered experience design to support Takeda’s ambition of becoming a data- and AI-driven enterprise. You will work closely with business units, global functions, platform teams, architecture, security, governance, and finance stakeholders to deliver scalable, secure, cost-transparent, and user-adoptable platform capabilities that improve decision-making and support the delivery of life-saving products to patients.
The role will help manage the evolution of enterprise data platforms from traditional data lake and integration capabilities toward intelligent, product-oriented, and experience-driven platforms. This includes supporting agentic AI patterns, Model Context Protocol (MCP), agent-to-agent (A2A) integration patterns, reusable automation frameworks, AI-assisted delivery methods, modern platform architecture, responsible FinOps practices, and intuitive experiences for business and technical users. The position will focus on operationalizing innovation, governance, usability, security, and cost accountability across enterprise data and AI services.
Responsibilities
AI/Agentic Enablement — 30%: Manage the adoption of agentic AI patterns, reusable AI agents, MCP-based context integration, A2A communication patterns, automation frameworks, prompt/workflow orchestration, and AI-assisted delivery practices across enterprise data platforms.
Solution and Platform Architecture — 30%: Manage scalable, secure, and resilient data and AI platform architecture capabilities across batch, streaming, real-time integration, data engineering, governance, and platform operations.
Tokenomics and FinOps — 30%: Manage cost transparency, token consumption governance, usage attribution, budget controls, optimization practices, and chargeback/showback models for AI, data, and cloud platform services.
UI/UX and Adoption — 10%: Work with product, design, and business stakeholders to improve platform usability, self-service onboarding, user journeys, and adoption experiences for technical and non-technical users.
Work with business engagement leads, enterprise architecture, data governance, security, finance, product, and engineering teams to translate business needs into pragmatic platform roadmaps and reusable solution patterns.
Manage modernization activities for enterprise data and AI capabilities, including data lakehouse, orchestration, integration, governance, security, observability, AI platforms, MCP/A2A enablement, and managed file transfer services.
Support operating models, standards, and guardrails for responsible AI usage, scalable platform delivery, consumption-based economics, and measurable business value realization.
Promote reliable platform operations focused on trust, reusability, continuous improvement, automation, cost optimization, and product-centric delivery.
Skills and Qualifications
Qualifications & Experience:
Education: Bachelor’s degree or higher in Computer Science, Information Technology, or equivalent work experience.
Technical Expertise: Strong understanding of AI-enabled platforms, agentic solution patterns, MCP, A2A, technology and solution architecture, FinOps/tokenomics governance, and scalable data platform design. Existing core technology skills remain applicable.
Experience: Minimum 10+ years of relevant experience in enterprise data platforms, solution architecture, platform modernization, cloud/data economics, AI-enabled delivery, and platform management.
AI, Architecture, FinOps & Platform Management:
Support responsible AI and agentic platform guardrails, including reusable design patterns, MCP-based context patterns, A2A orchestration approaches, governance checkpoints, and operational controls.
Manage solution and platform architecture alignment with enterprise standards, security, scalability, reliability, deployment, and release management expectations.
Establish tokenomics and FinOps practices to monitor usage, attribute platform and AI consumption, identify optimization opportunities, and support budget accountability.
Support product-centric delivery, self-service enablement, UI/UX thinking, digital dexterity, and measurable adoption practices.
Monitor portfolio progress, identify gaps, and provide strategic recommendations.
Stakeholder & Communication Management:
Collaborate with business and technology leaders, internal/external partners, data governance teams, and security teams to ensure alignment with Enterprise priorities.
Excellent communication and relationship-building skills to engage with both technical and non-technical stakeholders.
Ability to coordinate across teams, departments, and external partners to support delivery, adoption, and platform operations.
Core Technical Skills:
Note: The core technology skills below remain substantially unchanged and continue to represent the expected technical foundation for the role.
Data Engineering & Cloud Expertise:
Big Data Workloads: Experience in scaling and optimizing large-scale data solutions for performance and cost-effectiveness.
Cloud Architecture: Expertise in designing data solutions on AWS (or equivalent cloud services) with best practices in security and networking.
ETL/ELT & Data Integration:
5+ years in customer-facing technical roles with experience in ETL/ELT, Data Integration, Data Pipeline Orchestration, and Managed File Transfers (MFTs).
Hands-on experience with ETL/ELT/Orchestration tools (e.g., Informatica Data Management Cloud, Databricks, Tidal, Apache Airflow).
Experience working in distributed, big data environments.
SQL & Database Management:
Advanced working SQL knowledge and familiarity with relational and non-relational databases.
Expertise in building and optimizing big data pipelines, architectures, and datasets.
API, Messaging & Streaming:
Knowledge of API (Mulesoft), Messaging queue, pub/sub-stream processing, Streaming (Apache Kafka/Confluent Kafka)
DevOps & DataOps:
Hands-on experience with CI/CD, automation, infrastructure-as-code, and containerization.
Data Analytics & Root Cause Analysis:
Ability to analyze internal/external data and processes to drive business decisions and performance improvements.
Enterprise & Platform Management Skills:
Enterprise Data Environment:
Experience working in Enterprise Data Warehouses, Data Lakes (e.g., Databricks), Advanced Analytics, and Data Management.
Working experience with Data Quality, Data Privacy, Data Governance, Access and Identity (Entra, SailPoint)
Working knowledge MFT solutions (e.g., Hulft, GlobalScape, AWS Transfer).
Stakeholder Engagement:
Excellent communication and relationship-building skills to collaborate with cross-functional teams.
Ability to coordinate across technical and non-technical stakeholders and manage priorities through influence, transparency, and clear communication.
Data Platforms & Analytics:
Experience with Databricks Lakehouse (Delta Lake), Redshift, or equivalent data platforms.
Working knowledge of BI tools (PowerBI, Qlik, Tableau), REST API integrations, and SQL interfaces.
Nice-to-Have Technical Skills:
Programming & Cloud Technologies:
Experience with Java, Python, Spark, Hadoop, Kafka, SQL, NoSQL, PostgreSQL, or other modern programming languages.
Familiarity with DevOps & Development Tools: JIRA, Git, Jenkins, Bitbucket, Confluence.
Understanding of failover, high availability, and scalability in public cloud environments (AWS, Azure).
Nice to have knowledge on Immuta