Contract AI Data Engineer

Responsibilities

Own the data platform

• Own Bronze / Silver / Gold pipelines in Microsoft Fabric and OneLake
• Model governed semantic layers; serve Power BI via Direct Lake
• Ingest business-unit data into Fabric & Azure SQL with quality and lineage controls
• Enforce data governance, security, and PII handling aligned to Entra ID
• Expose curated datasets to AI agents through approved MCP servers only
• Use Claude Code to build pipelines against JSSI standards; review and validate all AI-generated code before it ships
• Orchestrate, monitor, and optimize pipelines for reliability, performance, and Fabric capacity (CU) cost
• Manage Git-based version control and Fabric deployment pipelines across dev / test / prod

Support reporting and ad-hoc requests

• Handle ad-hoc requests (report enhancements and fixes, and dataset enhancements and fixes) across Microsoft Fabric and Power BI
• Build and maintain paginated (operational) reports
• Write, debug, and tune SQL for reporting, data investigation, and fixes
• Use Claude Code to investigate and diagnose platform, data-quality, and reporting issues, tracing root cause across pipelines, lake house tables, and semantic models before drafting a validated fix
• Support and optimize Power BI semantic models and DAX for downstream consumers

Requirements

Required

• Hands-on experience building and maintaining data pipelines in Microsoft Fabric, including Lakehouse architecture and OneLake
• Proficiency with Bronze / Silver / Gold medallion architecture and semantic layer modeling
• Experience with Azure SQL, ADLS Gen2, and data ingestion across multiple source systems
• Understanding of data governance, security, and PII handling practices, including Entra ID alignment
• Experience exposing data products via APIs or MCP-style interfaces for downstream AI or analytics consumption
• Active use of AI coding tools (such as Claude Code) as part of your development workflow — not aspirational, but current practice

Strongly preferred

• Experience building data infrastructure specifically to support AI agents, ML models, or agentic pipelines
• Familiarity with Power BI and Direct Lake connectivity
• Experience working within engineering standards, documentation-first workflows, and structured code review practices
• Exposure to MCP (Model Context Protocol) servers or similar AI data access patterns

Not required

• People management experience — this is an individual contributor role
• Aviation or MRO domain knowledge — curiosity and the ability to learn the business context matters more than knowing it on day one

WHO THRIVES IN THIS ROLE

This role is a great fit for someone who takes genuine ownership of data infrastructure — not just someone who builds pipelines when tickets arrive, but someone who thinks about the platform as a product with real consumers. You are already using AI tools to write and ship code faster. You care about data quality as a first principle, not a nice-to-have. And you want to be part of building something that directly enables the AI-first future of a company, not supporting a legacy stack.