Senior Business Intelligence Engineer

Responsibilities

• Develop and maintain enterprise-wide dashboards and reports in Looker for business and cross-functional teams.
• Ensure data quality, consistency, and governance across all BI and analytics layers.
• Collaborate with business stakeholders to translate requirements into scalable analytics solutions.
• Optimize BI performance and data warehouse usage for speed, cost efficiency, and scalability.
• Define and enforce frameworks and best practices for dashboards, reporting, metric definitions, and data consumption.
• Design, build, and maintain core analytics datasets and dimensional data models to support reporting and decision-making.
• Build and support experimentation, funnel analysis, and measurement frameworks for key business initiatives.
• Mentor analysts and junior BI engineers to strengthen analytics capabilities and best practices.
• Conduct Looker office hours and training sessions to drive adoption and enablement.
• Enable self-serve analytics by improving data discoverability, documentation, and tooling.
• Partner with analytics engineering and data teams to ensure alignment between dbt models, ETL pipelines, and BI consumption.

Requirements

• 5+ years of experience in Business Intelligence, Analytics Engineering, or data modeling, with hands-on experience building and scaling analytics platforms.
• Strong experience with Looker / LookML development, visualization, and administration.
• Expert-level SQL skills and a deep understanding of dimensional data modeling and analytics-ready datasets.
• Experience partnering with dbt-modeled data and integrating with broader analytics engineering workflows.
• Strong understanding of data and BI governance, metric standardization, and self-serve analytics enablement.
• Comfortable owning and driving adoption of analytics platforms and data products, balancing immediate business needs with long-term maintainability.
• Excellent communication and collaboration skills, able to work effectively with both technical and non-technical stakeholders.

Nice to Have

• Experience defining metric layers or semantic models at scale.
• Exposure to experimentation, funnel, and customer lifecycle analytics.
• Experience modeling data using dbt.
• Familiarity with machine learning techniques and predictive analytics.
• Experience working with Fintech, banks or a B2C company.
• Experience running BI enablement sessions or office hours.