Senior Data Engineer

We’re building an AI-ready data platform on top of our BigQuery warehouse and looking for a Senior Data Engineer to lead how AI interacts with data.

This is a data engineering role with real AI exposure — focused on structure, semantics, and control, not model training.

You will define how data is understood, accessed, and used by AI systems across the company.

We use AI-native workflows (Claude Code).

A core part of the system is structured instruction layers (skill.md) that define:

• how AI queries data
• how metrics are interpreted
• what is allowed vs restricted

You will design both:

• the data layer
• and the instruction layer that makes it usable by AI

Data Platform

• Build clean, reusable analytical data layers in BigQuery
• Move business logic from BI into the warehouse
• Define metrics, dimensions, and semantic consistency

AI Interaction

• Enable reliable AI ↔ data interaction
• Design schemas + instructions so AI produces correct outputs
• Test and refine real AI usage (not theory)

Access & Governance

• Implement data-layer access control (not BI-layer)
• Row/column-level security, role/attribute-based access
• Ensure consistent behavior across BI, AI, and internal tools
• Ensure metric reconciliation across different data sources

Prevent:

• sensitive data leakage
• shadow metric layer
• uncontrolled query cost

Automation:

• Replace manual data workflows with AI-driven processes
• Build agents for reporting, validation, and internal analytics

• Strong SQL + data modeling (BigQuery or similar)
• Experience building scalable data layers (not just pipelines)
• Understanding of metrics, semantics, and analytical correctness
• Hands-on exposure to LLMs / AI tools (practical, not theoretical)
• Ability to design systems where data is consumed by machines

• dbt / semantic layers
• BigQuery performance & cost optimization
• Finance / brokerage data
• Fine-grained access control implementations
• AI agent / tool-based workflows

• You will define how AI uses data in a real business
• Work on non-trivial problems (semantics, access, correctness)
• Build systems from first principles, not maintain legacy
• High ownership