Platform Manager AI & Data Platforms

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• 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.

• Education: Bachelors 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.

• 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 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.

• 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