Senior Manager - Data & AI Solution Management

Sr. Manager – Data & AI Solution Management

Role Overview

The Sr. Manager – Data & AI Solution Management is a people leadership and execution role responsible for building, leading, and scaling a high-performing multidisciplinary data and AI COE in Global Business Services. This role oversees end-to-end service delivery and maintenance of enterprise data, analytics, AI/ML, and Generative AI solutions, ensuring alignment with business priorities, governance standards, and modern cloud architectures.

The position manages a team of senior professionals across analytics, engineering, AI, governance, and service management, driving service delivery while ensuring operational excellence, scalability, and responsible AI practices across AWS and Azure ecosystems.

Key Responsibilities

1. Strategic Leadership & Delivery

• Define and execute the enterprise Data & AI strategy, aligned with business goals and digital transformation initiatives.
• Lead the design and delivery of end-to-end data, analytics, ML, and GenAI solutions, ensuring business value realization.
• Establish scalable frameworks for AI/ML, data platforms, analytics, and governance across the organization.
• Drive adoption of modern data architecture patterns including cloud-native, multi-cloud, and hybrid ecosystems.

2. Team Leadership & Talent Development

• Lead, mentor, and develop a team of a medium group senior professionals across data, AI, engineering, and analytics domains.
• Build a high-performance culture focused on innovation, accountability, and continuous improvement.
• Define career paths, skill development, and succession planning for all roles.
• Foster cross-functional collaboration between business, engineering, and analytics teams.

3. Data & AI Solution Delivery Oversight

• Oversee delivery of solutions:
  • Data engineering pipelines and platforms (Databricks, Snowflake, AWS, Azure)
  • Data modeling and architecture frameworks
  • Advanced analytics and BI solutions
  • Machine Learning and Generative AI solutions (LLMs, RAG, copilots)
• Ensure integration of solutions into enterprise systems and workflows.
• Drive Agile delivery models and ensure timely, high-quality releases.

4. AI, ML & GenAI Enablement

• Establish scalable practices for:
  • Machine Learning lifecycle (MLOps)
  • Generative AI solution design (prompt engineering, RAG architectures)
  • AI evaluation, monitoring, and optimization
• Ensure AI initiatives are:
  • Business-driven
  • Measurable
  • Production-ready
• Partner with teams to embed AI capabilities into enterprise applications.

5. Data Governance, Quality & Compliance

• Ensure robust implementation of:
  • Data governance frameworks (metadata, lineage, cataloging)
  • Data quality and monitoring standards
  • Security, privacy, and regulatory compliance controls
• Promote trusted, governed, and high-quality data assets across the organization.
• Enable responsible AI practices including explainability, fairness, and compliance.

6. Stakeholder Engagement & Business Alignment

• Act as a trusted advisor to business and technology leaders.
• Translate complex business needs into scalable data and AI solutions.
• Drive adoption and value realization of delivered solutions.
• Lead executive reporting on program progress, outcomes, and KPIs.

7. Platform, Architecture & Technology Oversight

• Govern enterprise data and AI platforms including:
  • Databricks, Snowflake
  • AWS and Azure data services
  • SageMaker, Amazon Bedrock, Azure OpenAI
• Ensure solutions are:
  • Scalable, secure, and cost-efficient
  • Designed for performance and reliability
• Drive standardization, automation, and DevOps/CI-CD practices.

8. Service Management & Operational Excellence

• Partner with the DAIS Sr. Service Manager to ensure:
  • Stable operations of data and AI platforms
  • SLA adherence and incident management
  • Continuous improvement and monitoring frameworks
• Implement metrics-driven service management practices.

Required Skills & Experience

Leadership & Functional Expertise

• 12–15+ years of experience in data, analytics, AI/ML, or engineering roles, with at least 5+ years in leadership positions.
• Proven experience managing cross-functional teams across data, analytics, and AI domains.
• Strong understanding of:
  • Data engineering, modeling, and analytics
  • Machine learning and MLOps
  • Generative AI (LLMs, RAG, copilots)
  • Data governance and compliance frameworks

Technical Expertise

• Hands-on knowledge of:
  • Cloud platforms: AWS and/or Azure
  • Data platforms: Databricks, Snowflake
  • AI/ML platforms: SageMaker, Azure ML, Bedrock
• Familiarity with:
  • Data pipelines, ETL/ELT, and streaming architectures
  • Data modeling techniques (dimensional, relational, etc.)
  • AI/ML lifecycle, model evaluation, and deployment
  • Governance tools, metadata, and lineage frameworks

Business & Stakeholder Skills

• Strong ability to translate business problems into technical solutions.
• Excellent communication and executive presentation skills.
• Proven ability to influence senior stakeholders and drive transformation.
• Experience working in Agile and product-driven environments.

Education

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field.
• Master’s degree (MBA, Data Science, Analytics, or similar) preferred.