Staff Data Engineer

Analytics & Reporting

• Design, develop, and maintain reports and dashboards

• Build and optimize semantic models using strong dimensional modeling (star schema)

• Write and tune DAX measures with a focus on performance and usability

• Implement Power BI and Looker deployment pipelines and promote content across environments

Microsoft Fabric Platform

• Establish and maintain Microsoft Fabric architecture, including:

• 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).