Principal Insights Engineer

• Grow our ability to deploy ML Models and use the outputs in Business Intelligence tools.

• Grow our ability to deploy ML Models and use the outputs in Business Intelligence tools.
• Apply new tools or new features to existing business cases to determine the usability

• Convert data science models into production-ready forecasting systems that are versioned, monitored, documented, and reliable.

• Convert data science models into production-ready forecasting systems that are versioned, monitored, documented, and reliable.
• Promote product-level data quality with technical controls such as data quality checks, and automated monitoring to ensure forecasting runs as expected.
• Prototype new ways of consuming insights that go beyond dashboards.
• Build and enhance custom data applications (e.g., Streamlit apps or embedded analytics experiences) that make advanced analytics easy for business partners to use.
• Develop scalable approaches for integrating forecasts and advanced analytics into business workflows.

• Improve reliability of new analytical solutions by developing monitoring and alerting tools ensure insight products operate consistently.

• Improve reliability of new analytical solutions by developing monitoring and alerting tools ensure insight products operate consistently.
• Build tests and right-sized automation to reduce breakage and manual effort.
• Produce clear documentation for pipelines, interfaces, and forecasting workflows.

• Work closely with BI Lab leadership on prioritization and business alignment.

• Work closely with BI Lab leadership on prioritization and business alignment.
• Partner with data scientists on inputs/outputs, feature sets, and model expectations.
• Collaborate with IT and Data Teams to follow guidance on pipelines, compute, and tooling as well as to support scaled development.
• Ask detailed questions directly to clarify requirements when needed.

• Operate as an 50% builder / 50% guide.
• Deliver usable solutions quickly, with the understanding that internal IT partners can provide architectural refinement over time.
• Create initial patterns for forecasting, insight delivery, and lightweight applications that can scale as demand grows.

• 8-10 years of experience in BI tooling and experience with Machine Learning Models or similar roles with strong experience in data/analytics.

• 8-10 years of experience in BI tooling and experience with Machine Learning Models or similar roles with strong experience in data/analytics.
• Demonstrated experience building and deploying production-grade analytical tools. Including Experience operationalizing machine learning or forecasting models.
• Strong Python and SQL skills and ability to work with complex data structures.
• Experience implementing data quality checks, monitoring, alerting, or other observability practices.
• Ability to build internal-facing tools or applications using low code/no code apps
• Strong communication skills; able to ask clarifying questions and work independently with minimal specification.

• Familiarity with feature engineering pipelines.
• Exposure to CI/CD concepts for data or application deployment.
• Experience building internal tools or user interfaces for consuming analytics.
• Prior work in a global or cross-functional technical environment