At a glanceSummarised by Seekless from the posting.
Must have7
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
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
Nice to have4
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
Skills
Python
SQL
Machine Learning
CI/CD
Streamlit
Role Summary
The Principal Insights Engineer is the senior technical individual contributor responsible for turning analytical technology into scalable, reliable, and usable products for business stakeholders.
This role sits at the intersection of BI and Data Science with a flavor of Data Engineering; This role focuses on technology innovation, operationalizing forecasting, building internal applications for insight delivery.
This individual is expected to deliver value by building solutions, interfaces, and pipelines that bring analytical logic to life and allow business users to make better decisions at scale.
Their focus on the technology space allows them to drive innovation so that project leads, BI Developers, and other technical practitioners can use new tools and technologies to better deliver value to the business.
Key Responsibilities
Technical Innovation
•
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
Insight Product Engineering
•
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.
Reliability, Observability & Maintainability
•
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.
Collaboration & Technical Integration
•
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.
Hands-On Technical Execution
•
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.
Required Qualifications
•
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.
Preferred Qualifications
•
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