AI Analyst, BI

Job Summary

We are looking for an experienced AI & BI Automation Technical Lead to design, enhance and scale AI-powered Business Intelligence automation solutions.

The role combines hands-on Microsoft Power Platform, Python, Azure and Generative AI engineering with technical ownership of BI automation, automated quality validation and report delivery capabilities. The individual will convert business and operational requirements into scalable, reliable and reusable technology solutions.

The successful candidate will work closely with global BI hubs, Technology teams, architects and business stakeholders to automate BI processes, improve report quality, reduce manual effort and increase delivery efficiency. This role requires a strong balance of technical depth, BI domain understanding, solution ownership and stakeholder engagement.

Key Responsibilities

1. AI and BI Automation Solution Ownership

• Own the technical design, enhancement and ongoing evolution of AI-enabled BI automation capabilities.
• Develop scalable solutions for report onboarding, scheduling, refresh, monitoring, quality validation and delivery.
• Convert manual BI processes into reusable and configurable automation workflows.
• Define and maintain the technical roadmap with BI, Technology and business stakeholders.
• Evaluate AI and automation opportunities based on business value, feasibility, scalability and adoption potential.
• Ensure solutions can support multiple regions, clients, report types and BI hubs.
2. Python and Application Engineering

• Design and develop scalable, cloud-native applications using Python.
• Build and maintain APIs, backend services and business-rule engines.
• Develop reliable solutions using object-oriented programming, reusable components and established design patterns.
• Perform automated data validation, missing-value detection, duplicate analysis, anomaly detection and variance analysis using Pandas, Polars or equivalent frameworks.
• Develop integrations across BI reporting, automation, quality-control and delivery platforms.
• Integrate Power Platform applications with Azure services, SQL Server, REST APIs, SharePoint, Microsoft Teams, Power BI and other enterprise systems.
• Optimize applications for performance, reliability, maintainability and scalability.
3. Generative AI and Intelligent Automation

• Develop and integrate enterprise Generative AI solutions using services such as Azure OpenAI, Llama, LangChain and vector search.
• Build AI-enabled solutions using Azure AI Foundry, Azure AI Search, Microsoft Copilot Studio, AI Builder, custom prompts, agent flows and generative actions.
• Apply Retrieval-Augmented Generation, prompt engineering and structured AI workflows to BI use cases.
• Use AI to generate executive summaries, business insights, recommendations and exception explanations.
• Design controls to validate AI-generated output before it is used in business processes.
• Evaluate Small Language Models for cost-efficient or low-latency deployments.
• Explore AI agents and intelligent workflows for report creation, quality validation, monitoring and operational support.
• Apply deterministic validation wherever accuracy, traceability and repeatability are required.
4. BI Process and Quality Automation

• Understand the complete BI report lifecycle, including requirement intake, report creation, refresh, validation, approval and delivery.
• Automate repeatable activities across Excel-based reporting and other BI platforms.
• Develop configurable quality rules for data checks, business validation and change detection.
• Support report-level and worksheet-level checks such as blanks, duplicates, formula continuity, reporting periods, product or market changes and variance thresholds.
• Design monitoring and exception-management capabilities that enable users to identify and resolve failures quickly.
• Partner with BI subject-matter experts to convert business validation requirements into scalable technical rules.
• Establish measurable quality controls and audit trails for automated processes.
5. Microsoft Power Platform Engineering

• Design, develop and support enterprise applications using Power Apps, Power Automate and Dataverse.
• Build user interfaces, workflow automation, approval processes and data-driven applications aligned with BI operational requirements.
• Design secure Dataverse data models, roles, permissions and integration patterns.
• Apply Power Platform governance, Data Loss Prevention policies, environment strategy and reusable solution standards.
• Use custom connectors and APIs to extend Power Platform capabilities and integrate with enterprise services.
6. Architecture, Cloud and DevOps

• Contribute to scalable, extensible and secure solution architecture.
• Develop distributed, event-driven and microservices-based solutions where appropriate.
• Work with Azure, AWS or GCP cloud services based on approved enterprise architecture.
• Develop and integrate Azure capabilities including Azure Functions, Azure SQL, Azure Storage, Microsoft Entra ID and Azure Data Factory.
• Implement CI/CD pipelines, automated testing and version-control practices using Git.
• Manage Power Platform Application Lifecycle Management using solutions, connection references, environment variables, service principals and controlled DEV-to-TEST-to-PROD deployments.
• Support containerization and orchestration using Docker and Kubernetes where required.
• Apply Infrastructure as Code practices using Terraform, Pulumi or equivalent tools.
• Implement logging, monitoring and observability using Prometheus, Grafana, OpenTelemetry or equivalent tools.
• Ensure compliance with enterprise security, privacy, access-control and engineering standards.
7. Production Reliability and Operational Support

• Monitor application performance, automation success rates and production reliability.
• Lead technical investigation and root-cause analysis for production incidents.
• Implement preventive actions to reduce recurring failures and operational dependency.
• Define alerts, recovery mechanisms and escalation processes for critical failures.
• Maintain appropriate backup, versioning and recovery capabilities.
• Ensure solutions are documented and are not dependent on individual team members.
• Balance new feature development with application stability, maintainability and technical-debt reduction.
• Independently manage the complete product development lifecycle, from requirements and architecture through development, testing, deployment, monitoring and production support.
8. Global Stakeholder Engagement and Adoption

• Work with global BI hubs to understand operational requirements and identify automation opportunities.
• Translate business requirements into clear functional and technical specifications.
• Conduct solution demonstrations, technical walkthroughs and user-training sessions.
• Support hub onboarding, testing, feedback collection and adoption.
• Communicate solution capabilities, limitations, dependencies and rollout requirements clearly.
• Coordinate with BI, Technology, architecture, infrastructure, security and business teams.
• Build stakeholder confidence through transparent progress reporting and measurable outcomes.
9. Technical Leadership and Team Development

• Provide technical direction to developers and BI automation specialists.
• Review solution designs, application code and testing approaches.
• Establish coding, documentation, testing and deployment standards.
• Mentor team members in Python, AI, cloud engineering and BI automation.
• Support work prioritization, release planning and dependency management.
• Promote approved AI-assisted development tools, including GitHub Copilot, to improve productivity.
• Create reusable frameworks, components and engineering patterns that can be adopted across the BI organization.

Function: General Business
Experience Level: Mid-Senior Level