Senior Manager, Digital Transformation & Business Intelligence

Job description

The Head of Digital Transformation & Business Intelligence leads the enterprise-wide digital agenda and data-driven decision-making.

This role designs and executes the strategy to build the foundation (governance, visualization standards, workforce upskilling), enable and scale BI and automation, integrate AI-driven insights and predictive analytics, optimize digital experiences with GenAI-powered BI assistants, and institutionalize digital intelligence including monetizing analytics services and embedding ESG-aligned practices across operations.

A.

Corporate Digital Transformation

• Lead and execute digital transformation strategies in alignment with corporate objectives and value drivers.
  
  
• Digitize and automate end-to-end business workflows using process mining, RPA/low-code platforms, and integration services.
  
  
• Embed change management, agile delivery, design thinking, and experimentation to build a digital-first culture.
  
  
• Establish governance for digital initiatives with clear success metrics (OKRs/KPIs), benefits realization, and portfolio prioritization.
  
  
• Track and report on transformation KPIs and business impacts to senior leadership.
  
  

B.

Business Intelligence Strategy & Delivery

• Develop and implement a comprehensive BI strategy aligned with the enterprise data strategy.
  
  
• Define and maintain data visualization and dashboard standards; build a cross-domain enterprise BI dashboard library.
  
  
• Deliver self-service analytics capabilities, semantic models, and governed datasets for business stakeholders.
  
  
• Enable data literacy through training, communities of practice, and usage/adoption programs.
  
  
• Manage BI platform architecture, release lifecycle, and service management (SLAs, support, continuous improvement).
  
  

C.

Advanced Analytics & AI

• Define and execute the AI & advanced analytics roadmap (predictive, prescriptive, forecasting, optimization).
  
  
• Establish MLOps practices for model lifecycle: data preparation, feature engineering, training, deployment, monitoring, and governance.
  
  
• Integrate AI-driven insights into BI products; deploy GenAI-powered BI assistants and natural-language analytics.
  
  
• Champion responsible AI principles (fairness, transparency, privacy, security) and model risk management.
  
  
• Support data governance framework including ownership, quality standards, cataloging, lineage, and access controls.
  
  
• Partner with Data Architecture to deliver an enterprise data portal for governed self-service access (APIs, data marketplace).
  
  
• Maintain a unified semantic layer and metadata standards to ensure consistency across domains.
  
  
• Ensure compliance with data privacy, security, and regulatory requirements across jurisdictions.
  
  

E.

Digital & BI Center of Excellence (CoE) & Change Management

• Launch and lead the Digital & BI CoE, defining operating model, roles, processes, and funding.
  
  
• Provide coaching and enablement to business units; establish guilds/chapters for analytics and automation.
  
  
• Embed adoption within business processes through champions, governance forums, and incentives.
  
  

F.

ESG & Sustainability

• Integrate ESG data and metrics into BI and reporting; support frameworks (e.g., GRI, SASB) and assurance needs.
  
  

G.

Commercialization & Monetization of Analytics

• Develop and manage internal/external analytics products and services; define value propositions and pricing.
  
  
• Establish commercialization KPIs (usage, revenue, margin) and manage SLAs with business stakeholders and partners.
  
  

H.

Digital Experience & Adoption

• Enhance customer and employee digital experiences via journey mapping, personalization, and AI-enabled assistance.
  
  
• Measure and improve satisfaction (CSAT/NPS), task completion, and time-to-decision through digital channels.
  
  

I.

Risk, Compliance & Ethics

• Ensure compliance with relevant regulations and internal policies for data, AI, and digital platforms.
  
  
• Lead risk assessments for digital solutions; implement controls for model, data, and operational risks.
  
  

J.

Performance & Reporting

• Define measurement framework and dashboards to track digital transformation, BI adoption, automation gains, and business impact.
  
  
• Report progress to senior leadership and continuously refine the roadmap based on outcomes.
  
  

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Information Technology

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Required Skill Profession

Management & Operations, It & Technology