Sr. Business Intelligence Engineer

We are seeking a driven Senior Business Intelligence Engineer for our team. At Brady, this role functions as a hybrid Analytics Engineer and Data Application Developer, transforming analytics and enterprise data into production-grade tools.

You will bridge cloud data warehouses and functional software by building Python backend services, dynamic query engines, and automated API integrations to stream analytics, dynamic segmentation, and predictive models to business applications and external platforms.

Operating on a two-week sprint cadence, our team uses a 60/20/20 capacity model (Planned Work / Ad-Hoc / Protected Tech Debt Buffer) to safeguard your time for writing scalable code, maintaining backend services, and building data pipelines without operational overload.

• Data Application & Backend Engineering: Design, write, and maintain Python-based backend logic and RESTful APIs that turn data models and predictive rules into interactive software tools.
• Architecture & Dynamic Query Compilation: Construct and scale the Account 360 Medallion Architecture in cloud data platforms. Design programmatic SQL generation engines and asynchronous processing workflows to execute complex, multi-layered segmentation logic.
• Third-Party Integrations & Data Delivery: Develop and maintain automated data delivery connectors using REST APIs, native SDKs, and secure file transfer protocols to keep enterprise systems and external platforms continuously synchronized.
• Productionizing Analytical & Predictive Models: Work closely with statistical concepts to translate predictive models, scoring systems, and segmentation algorithms into performant, automated backend logic.
• Code Refactoring & System Health: Manage your workload within our 10-point sprint capacity model, utilizing your protected 20% tech debt buffer to continuously refactor codebases, optimize database execution performance, and maintain application architecture.
• Cross-Training & Collaboration: Actively participate in our cross-training framework, dedicating 4 hours per sprint to co-piloting engineering tasks and analytical techniques with peers.