Data & Analytics Leader-6

Role overview

Work model

• The posting specifies a hybrid work model in Toronto, Ontario.

Responsibilities

• Build relationships with CDOs, CIOs, CAIOs, business executives, and senior technology leaders.
• Advise BFSI clients on data strategy, analytics, AI adoption, governance, operating models, and modernization.
• Translate business challenges into roadmaps, investment priorities, and measurable outcomes; lead executive workshops and strategic discussions.
• Identify and close opportunities, shape consulting engagements, lead proposals and RFP responses, and develop partner opportunities.
• Define target-state strategies across cloud data platforms, data products, analytics, AI/ML, Generative AI, governance, and Responsible AI.
• Provide oversight from opportunity shaping through delivery and value realization, governing commitments, risks, milestones, quality, and communication.
• Lead and mentor consulting, architecture, engineering, analytics, and AI teams; support talent and capability development.
• Develop reusable offerings, reference architectures, case studies, estimators, presentations, and delivery playbooks.

Required experience

• 18+ years of experience across Data, Analytics, AI, Consulting, and Enterprise Data Transformation.
• 10+ years of experience in Data or Application Architecture.
• 5+ years of experience in Business Development, Presales, Consulting, or Growth Leadership.
• Strong expertise in enterprise data strategy, architecture, operating models, data products, governance, and modernization.
• Proven ability to deliver measurable ROI and business outcomes across the data-to-insights value chain.
• Strong understanding of data engineering, including ETL/ELT, integration, APIs, data quality, orchestration, observability, DataOps, and platform engineering.
• Experience leading enterprise analytics and BI initiatives, including reporting, self-service analytics, semantic layers, advanced analytics, and decision intelligence.
• Strong knowledge of AI/ML, Generative AI, Agentic AI, MLOps/LLMOps, and Responsible AI.

Technology and domain focus

• Target-state strategies may cover Azure, AWS, GCP, Databricks, Snowflake, and other modern data platforms.
• Solutions are expected to address scalability, security, privacy, resilience, cost optimization, auditability, and regulatory compliance.
• The role serves Banking, Financial Services, and Insurance clients.