Gen BI Director

Axtria - Ingenious Insights; Bengaluru; Sur place; INR 3 500 000 - 5 200 000; Plein temps

Résumé du poste

Axtria - Ingenious Insights in Bengaluru, India, seeks a senior, hands-on BI + AI Architect with 16–20 years of experience to lead enterprise analytics modernization and deliver production-grade AI-enabled BI solutions.

You will own end-to-end BI/AI platforms (Power BI, Tableau, Fabric, Snowflake, Databricks), design governed semantic models and KPI layers, and drive AI copilots, agentic workflows, and decision-support for executives.

Qualifications

• 16–20 years of experience across BI, analytics architecture, data platforms, enterprise reporting, AI-enabled analytics, solution consulting, or digital transformation.
• Strong architecture and hands-on expertise in Power BI and at least one additional BI platform such as Tableau, ThoughtSpot, Qlik, MicroStrategy, or Looker.
• Experience with Microsoft Fabric, Snowflake, Databricks, Azure / AWS, SQL, dimensional modeling, semantic models, DAX, performance tuning, data governance, and deployment lifecycle management.
• Deep understanding of GenAI and Agentic AI including LLMs, RAG, vector search, embeddings, prompt engineering, model evaluation, tool use, memory, orchestration, multi-agent workflows, and human-in-the-loop controls.
• Practical experience rolling out AI applications, copilots, AI assistants, decision-support platforms, conversational analytics, or autonomous workflow agents in production environments.
• Ability to integrate AI capabilities with enterprise data, APIs, business applications, BI platforms, knowledge bases, workflow tools, and security / identity frameworks.
• Strong stakeholder management and executive communication skills, with ability to simplify technical architecture into business value narratives.

Responsabilités

• Own end-to-end architecture for enterprise BI and AI-enabled analytics platforms across Power BI, Tableau, Microsoft Fabric, Snowflake, Databricks, Azure / AWS, and modern lakehouse ecosystems.
• Design governed semantic models, KPI layers, reusable data products, access controls, performance patterns, and deployment standards for scalable BI delivery.
• Modernize legacy reporting estates into AI-ready, decision-centric analytics platforms with strong governance, adoption, and business value focus.
• Architect GenAI and Agentic AI solutions using LLMs, RAG pipelines, vector search, knowledge graphs, prompt engineering, model evaluation, orchestration, and responsible AI guardrails.
• Design AI copilots, conversational BI, autonomous insight workflows, KPI explanation engines, decision-support assistants, and multi-agent processes that integrate with enterprise data and applications.
• Lead AI application rollout across discovery, prototype, MVP, production deployment, user adoption, telemetry, monitoring, cost optimization, and continuous improvement.
• Create architecture blueprints, solution designs, reusable accelerators, POCs, MVPs, and demo assets for BI, GenBI, and Agentic AI use cases.
• Work closely with business, data engineering, AI engineering, UX, security, delivery, and product teams to convert use cases into reliable and governed solutions.
• Ensure AI solutions are secure, explainable, observable, compliant, and aligned with enterprise privacy, access, lineage, and audit requirements.

Connaissances

Enterprise BI architecture

GenAI & Agentic AI

Power BI

Tableau

Snowflake

Databricks

LLMs

RAG pipelines

Performance tuning

Executive communication

Outils

Power BI

Tableau

Microsoft Fabric

Snowflake

Databricks

Azure

AWS

Description du poste

We are looking for a senior, hands-on BI + AI Architect with 16-20 years of experience to lead enterprise analytics modernization and build production-grade AI-enabled BI solutions. The role requires deep BI architecture expertise, strong working knowledge of GenAI and Agentic AI, and proven experience taking AI applications from concept and prototype to enterprise rollout. The candidate should be able to design scalable semantic layers, governed KPI frameworks, conversational analytics, RAG-enabled knowledge experiences, AI copilots, multi-agent workflows, and decision-support platforms for business and executive users.

Key Responsibilities

1. Enterprise BI Architecture & Modernization

• Own end-to-end architecture for enterprise BI and AI-enabled analytics platforms across Power BI, Tableau, Microsoft Fabric, Snowflake, Databricks, Azure / AWS, and modern lakehouse ecosystems.
• Design governed semantic models, KPI layers, reusable data products, access controls, performance patterns, and deployment standards for scalable BI delivery.
• Modernize legacy reporting estates into AI-ready, decision-centric analytics platforms with strong governance, adoption, and business value focus.

2. GenAI, Agentic AI & AI Application Architecture

• Architect GenAI and Agentic AI solutions using LLMs, RAG pipelines, vector search, knowledge graphs, prompt engineering, model evaluation, orchestration, and responsible AI guardrails.
• Design AI copilots, conversational BI, autonomous insight workflows, KPI explanation engines, decision-support assistants, and multi-agent processes that integrate with enterprise data and applications.
• Lead AI application rollout across discovery, prototype, MVP, production deployment, user adoption, telemetry, monitoring, cost optimization, and continuous improvement.

3. Hands-on Solutioning, Prototyping & Delivery Enablement

• Create architecture blueprints, solution designs, reusable accelerators, POCs, MVPs, and demo assets for BI, GenBI, and Agentic AI use cases.
• Work closely with business, data engineering, AI engineering, UX, security, delivery, and product teams to convert use cases into reliable and governed solutions.
• Ensure AI solutions are secure, explainable, observable, compliant, and aligned with enterprise privacy, access, lineage, and audit requirements.

4. Pharma Analytics, Business Context & AI Use Case Translation

• Translate business problems into BI, GenAI, and Agentic AI opportunities with clear value linkage across productivity, speed-to-insight, adoption, and decision quality.
• Shape use cases such as field insight assistants, market access copilots, brand performance advisors, HCP / customer intelligence copilots, launch planning agents, and narrative insight generators.
• Apply regulated-industry expectations including data privacy, consent, explainability, auditability, governance, and responsible AI-by-design.

5. Client Advisory, Executive Communication & Thought Leadership

• Engage senior business, data, digital, IT, analytics, and AI stakeholders to shape roadmaps, architecture direction, solution trade-offs, and adoption plans.
• Lead client workshops, architecture reviews, technical discovery, solution walkthroughs, executive readouts, and AI / BI demos.
• Communicate complex BI and AI topics in a concise, executive-ready manner with strong focus on outcomes, risks, and measurable business value.

6. Technical Leadership, Governance & Capability Building

• Provide technical leadership to BI architects, data engineers, AI engineers, product owners, and delivery teams.
• Review solution designs, semantic models, dashboards, RAG patterns, agent workflows, prompt designs, and AI governance controls for quality and scalability.
• Mentor teams on BI architecture, AI solution design, agentic patterns, delivery excellence, reusable assets, and responsible AI practices.

Required Qualifications & Skills

• 16-20 years of experience across BI, analytics architecture, data platforms, enterprise reporting, AI-enabled analytics, solution consulting, or digital transformation.
• Strong architecture and hands-on expertise in Power BI and at least one additional BI platform such as Tableau, ThoughtSpot, Qlik, MicroStrategy, or Looker.
• Experience with Microsoft Fabric, Snowflake, Databricks, Azure / AWS, SQL, dimensional modeling, semantic models, DAX, performance tuning, data governance, and deployment lifecycle management.
• Deep understanding of GenAI and Agentic AI including LLMs, RAG, vector search, embeddings, prompt engineering, model evaluation, tool use, memory, orchestration, multi-agent workflows, and human-in-the-loop controls.
• Practical experience rolling out AI applications, copilots, AI assistants, decision-support platforms, conversational analytics, or autonomous workflow agents in production environments.
• Ability to integrate AI capabilities with enterprise data, APIs, business applications, BI platforms, knowledge bases, workflow tools, and security / identity frameworks.
• Strong stakeholder management and executive communication skills, with ability to simplify technical architecture into business value narratives.