Staff Data Engineer

Pearson•Durham, NC

About The Position

We are seeking an Analytics Engineer to design, build, and operate our analytics and automations as well as build of AI-powered automations and copilots using governed enterprise data. This role is responsible for delivering high-quality Power BI reporting, establishing and maintaining Microsoft Fabric and/or GCP BigQuery, and building business automations and applications using Python, Power Automate and Power Apps. You will be part of the Cloud & Service Management organization helping to evolve our self-service analytics, scalable data architecture, and automations—while ensuring security, performance, and governance across the platform. This is a hands-on role with ownership of both solution delivery and platform best practices.

Requirements

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

Nice To Haves

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.

Responsibilities

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
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
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
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
Translate business requirements into scalable technical solutions
Contribute to platform roadmap and continuous improvement efforts
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).