JSSI is building an AI-first data platform, and the AI Data Engineer is the person who builds and owns it. You are the platform owner — responsible for the pipelines, the lakehouse, and the governed datasets that make AI and analytics possible across every JSSI business unit.
This is a hands-on, individual contributor role. You will design and build the data infrastructure that AI agents depend on, ensure data quality and governance are production-grade, and work at the intersection of data engineering and AI delivery. You will use Claude Code to build pipelines against JSSI standards and be a core part of how the organization accelerates.
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.
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