The opportunity

EYs Global Tax Centre of Excellence Data & AI team play a prominent role in leading client transformation initiatives that leverage Data & AI as core capabilities.  The CoE seeks to architect reusable patterns and process blueprints to bootstrap scalability across all the EY Tax technology teams in all key markets.

You will have an existing opportunity to stay on the leading edge of technology development and to contribute to the successful transformation of EY tax and its clients.

Role Purpose

The Data & AI Lead role is responsible for designing and architecting agentic tax workflows, underpinned by AIready data products and robust data engineering.

This role sits at the intersection of:

• Tax domain understanding
• Data architecture and engineering
• Applied AI and agent orchestration

It ensures that agents are fed with trusted, timely, wellstructured data and are deployed in a way that scales across clients and platforms.

Key Responsibilities but not limited to:

Agentic Workflow & AI Design

• Design agentic workflows for tax processes, including:
  • tasklevel agent responsibilities
  • agenttoagent communication patterns
  • humanintheLoop and escalation models
  • feedback loops and agent performance monitoring and evaluations
  • Define reusable agent blueprints aligned to Leanderived task definitions
  • Apply leading edge agentic driven design and build methodologies eg.  use of Codex, Factory ai, GitHub Copilot

Data Architecture & AIReady Data Products

• Design and oversee the build of AIready data marts that:
  • expose tax data at the right grain
  • maintain lineage and quality controls
  • support deterministic and probabilistic reasoning
• Lead data ingestion and transformation from:
  • ERP systems
  • tax platforms
  • semistructured documents
  • external data sources

Agent Governance & Security

• Apply design led thinking to embed agent and workflow governance and security:
  • Ensure compatibility and integration with clients governance models and technology (eg. Microsoft Agent 365)
  • Design and embedded real time controls and risk monitoring

Platform Strategy

• Architect solutions deployable:
  • within client environments (e.g. ServiceNow with workfloworchestrated agents), and
  • within EYhosted platforms
• Ensure EY retains reusable IP through:
  • agent blueprints
  • data mart patterns
  • orchestration logic
  • delivery assets eg. design and build agents
  • security & governance blueprints

CoE & Asset Development

• Act as technical authority within the CoE
• Develop reference architectures, patterns, and accelerators
• Support PoCs, pilots, and scaled deployments

Required Experience & Background

• Bachelor's degree in computer science engineering or equivalent work experience. Advanced certifications in AI, machine learning, cloud computing or data engineering are highly advantageous.
• 15+ years experience across data architecture, data engineering, and analytics including building SQL database, database designing and data modelling.
• Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph.
• Strong grounding in:
  • data modelling
  • data governance and quality
  • largescale data integration
• Handson experience applying AI/ML or advanced analytics in production environments
• Proficient in programming or scripting languages: for instance, C++, Java, Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers).
• Comfortable working with both business stakeholders and data & ai engineers
• Experience shaping technical strategy and operating models
• Strong verbal and written communication skills
• Excellent problem solving skills and unsatiable desire to learn and keep up with technology developments.

Desirable Experience

• Prior work in Tax Technology or Finance platforms
• Experience with agentic AI concepts, orchestration, or workflowdriven automation
• Exposure to cloudbased data platforms and modern AI stacks

Success Measures

• Quality and reusability of AIready data marts
• Stability and scalability of agent deployments
• Speed from Lean design to working agent
• Reuse of assets across clients and service lines