Data Engineering And Analytics - Business Analyst

Key Responsibilities:

• Lead discovery and requirements definition for data, analytics, and AI initiatives across Clients & Industries. - Partner with senior stakeholders to shape business problems into clear analytics and AI use cases with defined success measures. - Translate business needs into structured requirements, user stories, and acceptance criteria aligned to delivery backlogs. - Act as a trusted advisor, balancing strategic objectives with execution-focused delivery. - Contribute to C&I data strategy by identifying priority data domains, decision journeys, and analytics opportunities. - Define and govern enterprise KPIs, metrics, and reporting standards to ensure consistency and trust. - Maintain business glossaries, metric definitions, and analytics standards aligned with data governance practices. - Define data collection requirements including sources, granularity, frequency, ownership, and quality expectations. - Identify data gaps, quality issues, and remediation needs. - Establish validation rules and trusted data criteria for analytics and AI use cases. - Partner with analytics teams to deliver actionable insights and executive-ready narratives. - Validate dashboards, reports, and AI outputs for accuracy, relevance, and business alignment. - Drive adoption through enablement, walkthroughs, and targeted stakeholder communication. - Identify optimization opportunities to enhance analytics and AI impact. - Provide functional guidance to junior Data Engineers and ensure clarity of requirements and priorities. - Collaborate across global, multi-disciplinary C&I teams to deliver outcomes. Qualifications Required:

• AI-first mindset with strong interest in Copilot-led solutions and intelligent automation. - Strong Business Analysis capability in data- and analytics-heavy environments. - Ability to balance strategic thinking with hands-on delivery and execution rigor. - Executive presence and confidence engaging senior stakeholders as a trusted partner. - Strong relationship-building and influencing skills across business and technology teams. - Excellent written and verbal communication skills with the ability to simplify complexity. - Strong judgment, structured thinking, and operational discipline in a fast-paced environment. - Ability to operate effectively across geographies, cultures, and service lines. (Note: Company details have been omitted as they were not explicitly mentioned in the provided job description.) Role Overview: As a Business Analyst (Data, Analytics and AI) at EY, you will play a pivotal role in shaping, governing, and delivering data-driven and AI-enabled solutions for Clients & Industries (C&I) and select EY-wide initiatives. You will be part of the Data Strategy team, operating at the intersection of business, analytics, and AI to ensure that Analytics and Copilot-led agentic solutions are aligned to strategic objectives and deliver measurable business value. This role combines hands-on delivery with ownership of analytics and AI governance, operating models, and value realization. You will act as the Business Analyst on Data Analytics and AI initiatives, while also contributing to data strategy, metric standardization, and trusted data foundations across C&I. Key Responsibilities:

• Lead discovery and requirements definition for data, analytics, and AI initiatives across Clients & Industries. - Partner with senior stakeholders to shape business problems into clear analytics and AI use cases with defined success measures. - Translate business needs into structured requirements, user stories, and acceptance criteria aligned to delivery backlogs. - Act as a trusted advisor, balancing strategic objectives with execution-focused delivery. - Contribute to C&I data strategy by identifying priority data domains, decision journeys, and analytics opportunities. - Define and govern enterprise KPIs, metrics, and reporting standards to ensure consistency and trust. - Maintain business glossaries, metric definitions, and analytics standards aligned with data governance practices. - Define data collection requirements including sources, granularity, frequency, ownership, and quality expectations. - Identify dat