Lead, Business Intelligence Engineer

Job Classification

Technology - Engineering & Cloud

This hybrid role bridges advanced statistical modeling with robust data pipeline engineering to turn raw, complex data into reliable, business-aligned datasets and predictive insights that drive decision making and operational excellence. It spans the full analytics lifecycle—from data acquisition and exploratory analysis to model development, validation, and production deployment—while applying software engineering practices (version control, CI/CD) and strong governance.

• Collaborate with business units to identify, scope, and prioritize AI/ML opportunities that align with organizational goals.
• Visualize and communicate findings to technical and non‑technical stakeholders to support informed decisions.

• Acquire, organize, and analyze large/complex datasets using statistical and computational techniques.
• Develop, train, and validate predictive models and machine‑learning algorithms; measure model performance and business impact.
• Stay current with industry trends, tools, and best practices in data science, ML, and AI.

• Design, build, and maintain reliable data pipelines and transformations that produce clean, reusable datasets for analytics and ML.
• Apply software engineering rigor (version control, CI/CD); document business logic and data lineage; enforce data quality standards.
• Use SQL‑ and Python/R‑based tooling (e.g., dbt for transformations) to operationalize analytics at scale.

• Leverage cloud analytics platforms (e.g., AWS Sage Maker, Azure ML) and visualization tools (e.g., Power BI, Tableau) to deliver solutions at enterprise scale.

• Ensure data use adheres to information security standards (least privilege access) and approved change‑management practices for models and pipelines.
• Maintain confidential/restricted data only in approved production environments; obtain Information Owner approvals for data requests/transfers.
• Support business continuity and disaster recovery by ensuring uninterrupted analytics service coverage across locations and teams.

• Advanced proficiency in Python and/or R for analysis and ML.
• Strong SQL for ETL and transformation; experience with data visualization tools (Power BI, Tableau).
• Solid foundation in statistics, mathematics, and computer science.
• Familiarity with version control (Git) and CI/CD pipelines; ability to communicate complex analytical concepts to diverse audiences; strong business acumen.

• Hands‑on experience with dbt and modern analytics engineering practices; enterprise BI and data‑lake tooling (e.g., Fabric/Power BI/Excel).
• Experience deploying ML solutions on AWS Sage Maker and/or Azure ML; operating within enterprise controls.

• Minimum of 5 years professional experience in data science, analytics, or a related quantitative field (advanced degrees may substitute for some experience).
• Proven track record of developing, deploying, and maintaining ML models and data pipelines in a business environment; cross‑functional delivery experience.
• Prior experience with cloud‑based analytics platforms and modern data engineering practices.

• Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or related quantitative field required.
• Advanced degrees (Master's/Ph.D.) preferred and may substitute for some work experience; equivalent work‑related skill/knowledge is valued.

• Obtain Information Owner approval for all data requests/transfers; implement and monitor models/AI systems per company security standards.
• Maintain all confidential/restricted data in approved production environments; follow approved change‑management processes for updates and deployments.

• Enhance decision making through trusted, timely insights; drive innovation in customer experience, risk management, and operational efficiency; support business continuity; and help mitigate reputational/customer risks via accurate analysis.

• Lo…