Coordinate contributors across business and technology teams to drive alignment, accountability, and timely execution
Provide day-to-day leadership, coaching, and guidance to business analysts and project contributors supporting GDP initiatives
Ensure delivered solutions stay aligned with GDP strategy, enterprise architecture standards, and business priorities
Business & Data Requirements Analysis
Translate business use cases into well-defined product requirements, user stories, and acceptance criteria
The team
The Data Strategy & Governance Office (DSGO) is responsible for building an integrated strategy and operating model to effectively and consistently manage global data and AI throughout its lifecycle to more effectively serve our clients, meet business objectives and foster innovation. The team is leading initiatives to develop a data and AI strategy and establish consistent data governance, data management, and AI product development and governance practices at Deloitte Global, enabled by technology. The team reports to the Global Chief Information Officer under the Technology Strategy and Architecture portfolio.
Qualifications
Do you possess the following?
6+ years in Business Analysis, Business Systems Analysis, Data Analysis, Data Product Management, or a related discipline
University degree, or equivalent working experience, in Business, Computer Science, or Technology
Data & Delivery Expertise
Track record leading data initiatives end-to-end within a product-driven data organization, from discovery through delivery and adoption
Experience defining business and technical KPIs and success measures for enterprise data products and analytics solutions
Skilled at producing source-to-target mapping documentation, data lineage artifacts, and transformation logic
Working knowledge of data platforms, data warehousing, ETL/ELT processes, and analytics ecosystems such as Databricks or Snowflake
Experience with enterprise systems such as SAP, Salesforce, Informatica, or similar operational platforms
AI & Emerging Technology Fluency
Practical grasp of how data fuels AI outcomes, particularly within a professional services context, and what makes data “AI-ready”
Familiarity with agentic AI concepts and integration patterns such as Model Context Protocol (MCP), and how they let AI systems draw on internal enterprise data
A platform mindset — an instinct for when to build once and scale versus solve narrow
Communication & Stakeholder Skills
