Lakehouse and/or Warehouse
Dataflows Gen2
OneLake data organization
Manage Fabric capacities, workspaces, and permissions
Monitor performance, cost, and reliability of Fabric workloads
Develop and maintain Python-based data transformations and notebooks within Fabric
Use Python for data preparation, enrichment, validation, and advanced analytics
Define and enforce data modeling and medallion architecture standards
Automation & Applications
Build and maintain automation flows for business processes, approvals, and integrations
Work with Dataverse, connectors, and security roles
Implement error handling, logging, and operational support patterns
Platform Governance & Operations
Define Dev/Test/Prod environment strategy for reporting and automation platform
Implement Application Life best practices (solutions, pipelines, source control where applicable)
Establish governance standards to prevent platform sprawl
Partner with security and IT teams on access control and compliance
Provide guidance and enablement to analysts and citizen developers
Collaboration & Leadership
Translate business requirements into scalable technical solutions
Contribute to platform roadmap and continuous improvement efforts
Agentic AI & ML Enablement
Design and deliver agentic AI solutions that automate multi-step business workflows (tool use, planning, and human-in-the-loop approvals) using enterprise data and governed actions.
Build RAG (retrieval-augmented generation) patterns over Fabric/OneLake (document ingestion, chunking, embeddings, retrieval evaluation) to power analytics copilots and self-service Q&A.
Develop and operate ML pipelines (feature engineering, training, evaluation, batch/real-time inference) using Python and approved ML frameworks.
Establish LLMOps/ModelOps practices: prompt/version control, offline evaluation, regression testing, monitoring (quality, drift, cost, latency), and safe rollback.
Implement AI security and governance: data access controls, prompt/data leakage prevention, PII handling, model risk reviews, and audit logging for agent actions.
Partner with stakeholders to identify high-value use cases and deliver measurable outcomes (time saved, defect reduction, SLA improvements).
Required Qualifications
5+ years of experience in analytics, BI, or data engineering roles
3+ years of hands-on Power BI development experience
Strong experience with Microsoft Fabric (Lakehouse, Warehouse, Dataflows)
Proficient in DAX, SQL, and data modeling
Hands-on experience with
Power Automate (cloud flows, approvals, integrations)
Power Apps (Canvas apps)
Dataverse
Hands-on Python experience delivering ML or GenAI solutions in production (notebooks-to-service, APIs, scheduled jobs, or integrated automations).
Working knowledge of RAG concepts (embeddings, vector search, retrieval, grounding, evaluation).
Experience implementing monitoring and testing for data/ML/GenAI systems (data quality checks, model/prompt evaluation, logging/telemetry).
Experience managing environments, security, and deployments
Strong understanding of data governance and analytics best practices
Preferred Qualifications
Experience designing enterprise-scale analytics platforms
Familiarity with Azure services (Azure SQL, Data Factory, Synapse)
Familiarity with GCP BigQuery and Looker
Experience with CI/CD concepts for Power BI and Looker
Power Platform or Microsoft analytics certifications
Experience working in a Center of Excellence (CoE) model
Experience with Azure OpenAI / Azure AI Foundry (or equivalent) and enterprise deployment patterns.
Experience with orchestration frameworks (e.g., Semantic Kernel, LangChain, Autogen) and tool/function calling.
