Lead complex data initiatives by transforming ambiguous business needs into scalable architectures, pipelines, and analytical models that deliver impact.
Partner with Product Management, Product Engineering, and Analytics to understand requirements and deliver analytics platform capabilities for both external and internal stakeholders.
Drive key semantic modeling design decisions that balance current reporting needs with future scalability and adaptability.
Design, train, and operationalize ML models for use cases such as forecasting, personalization, recommendations, and anomaly detection. Integrate these models into production pipelines and ensure continuous improvement through experimentation and monitoring.
Establish and enforce best practices for data governance, cataloging, lineage tracking, and quality assurance across the analytics and ML ecosystem.

Bachelor’s degree or higher in Computer Science, Information Technology, Data Science, or a related discipline
8+ years of professional experience in the data domain — including roles such as Analytics Engineer, Data Engineer, Data Scientist, or similar
Proven track record of delivering customer-facing reporting, dashboards, and analytics capabilities in partnership with Product teams
Deep expertise with modern data stack technologies such as dbt, Snowflake, SQL, Python, and Looker/Omni
Strong understanding of modular and reusable data modeling best practices (e.g., star and snowflake schema design)
Comprehensive knowledge of data governance, data quality frameworks, and analytics/security best practices
Excellent problem-solving, communication, and cross-functional collaboration skills, with experience working on global teams

Staff Analytics Engineer

Key skills