EY - GDS Consulting - AI And DATA - Semantic BI - Manager

Job Summary

Role: Semantic BI Engineer

Experience Guide: Guide / 7-12 years

Primary Skill Area: Semantic Analytics, Self-Service BI, Dashboards, AI-Enabled Insights Decision Intelligence

The opportunity: Design, build and advise on enterprise BI and semantic analytics solutions that convert trusted data into actionable business insights. This role is not limited to dashboard development; the candidate must be able to assess business needs, propose the right dashboarding or self-service reporting tools, design semantic data layers, build high-quality dashboards, and enable next-generation AI-assisted analytics experiences. The role combines analytics engineering, semantic layer design, dashboard architecture, BI strategy, data storytelling, data modelling, AI enablement and strong business analysis capability.

Your key responsibilities

• BI Strategy, Tool Evaluation Solution Advisory
• Assess business reporting, analytics and self-service consumption needs across user personas, maturity levels, governance requirements and operating constraints.
• Evaluate and recommend the right BI, dashboarding and self-service reporting tools based on scalability, semantic layer fit, governance, cost, user adoption, integration capability and AI-readiness.
• Shape enterprise BI and analytics roadmaps across Power BI, Microsoft Fabric, Tableau, ThoughtSpot, Qlik, Databricks AI/BI, Snowflake Cortex Analyst and other modern analytics platforms.
• Advise stakeholders on when to use operational reporting, executive dashboards, self-service analytics, semantic search, conversational BI, embedded analytics or AI-assisted insight discovery.
• Semantic Layer Analytics Engineering
• Design semantic data layers that separate business logic from physical data structures and allow consistent consumption across dashboards, reporting tools, AI assistants and self-service analytics platforms.
• Define governed business metrics, KPIs, measures, dimensions, hierarchies, calculations, data definitions and reusable analytical entities.
• Develop analytics engineering assets using SQL, dbt-style transformation patterns, Fabric semantic models, Power BI datasets, lakehouse/warehouse tables and curated analytical data products.
• Align semantic models with business glossaries, metadata, lineage, data quality rules, access controls and enterprise governance standards.
• Dashboard Development, Architecture Data Storytelling
• Design and develop high-quality dashboards, scorecards and analytical reports that support executive decisions, operational monitoring, root-cause analysis and business performance tracking.
• Apply dashboard design principles including KPI hierarchy, drill-through, guided navigation, narrative flow, visual consistency, usability, accessibility and performance optimisation.
• Build dashboards using tools such as Power BI, Fabric, Tableau, ThoughtSpot, Qlik, Databricks AI/BI, Snowflake Cortex Analyst or equivalent enterprise BI platforms.
• Move beyond visualisation by translating patterns, trends and exceptions into actionable business insights, recommendations and decision support.
• Data Modelling, Data Analysis Insight Generation
• Apply strong data modelling skills including dimensional modelling, star schema design, semantic modelling, analytical data product modelling, metric modelling and data mart design.
• Perform deep data analysis using SQL, analytical thinking, data profiling, reconciliation, segmentation, trend analysis, variance analysis and root-cause investigation.
• Partner with business SMEs, data engineers, data architects and product owners to convert business questions into robust data models, analytical logic and reusable reporting assets.
• Ensure insights are grounded in trusted data, explainable logic, meaningful context and clear business interpretation.
• AI-Enabled Analytics Next-Generation BI
• Understand where and how AI can improve analytics workflows, including natural language querying, automated insight generation, anomaly detection, narrative summaries, semantic search and guided analysis.
• Enable AI-assisted analytics using capabilities such as Fabric Copilot, Power BI Copilot, Databricks Genie, Databricks AI/BI, Snowflake Cortex Analyst, ThoughtSpot Spotter, Azure AI Foundry and Azure OpenAI where relevant.
• Design AI-ready semantic layers that provide business context, trusted metrics, governed definitions and explainability for conversational BI and agentic analytics use cases.
• Identify appropriate use cases for AI in BI while recognising where traditional dashboards, governed reports or human-led analysis remain more suitable.
• Governance, Adoption Analytics Operating Model
• Define governance standards for dashboards, semantic models, KPIs, certified datasets, access controls, refresh schedules, lifecycle management and reporting asset ownership.
• Support integration with governance and catalogue platforms such as Microsoft Purview, Collibra, Unity Catalog, Snowflake Horizon, Immuta or equivalent enterprise tools.
• Create templates, design standards, dashboard review checklists, documentation patterns and adoption playbooks for consistent analytics delivery.
• Enable business users through self-service analytics training, guardrails, metric definitions and trusted data consumption practices.

Skills and attributes for success

• BI and dashboarding - Power BI, Microsoft Fabric, Tableau, ThoughtSpot, Qlik, Databricks AI/BI, Snowflake Cortex Analyst, executive dashboards, self-service reporting, embedded analytics
• Semantic and analytics engineering - Semantic layers, Fabric Semantic Models, Power BI datasets, metrics layer, governed KPIs, SQL, dbt-style modelling, analytical data products, business glossary alignment.

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