Analytics Engineer building trusted data products for Upside’s core data platform. Collaborating with teams to improve analytics platform and enabling high-quality data work.
Responsibilities
Design and build complex, scalable data products that support internal analytics and power product features, using tools like dbt, Snowflake, and Dagster.
Translate ambiguous business needs into structured, trustworthy data assets—owning development end-to-end, from design through deployment and monitoring.
Contribute to shared platform tooling that improves pipeline orchestration, testing, access control, and observability for both engineers and analysts.
Evaluate and implement new platform capabilities, such as Semantic Views, Cortex, or SnowML, to improve performance, maintainability, and user experience.
Support and enable teammates across the org, including onboarding new users, resolving data issues, and documenting patterns and best practices.
Maintain reliability and data quality through shield support rotations, strong monitoring, alerting, and test coverage.
Requirements
Have 2–4 years of experience working in data or analytics engineering roles, preferably in a modern data stack environment (e.g., Snowflake, dbt, Dagster, Airflow).
Are fluent in SQL, and comfortable writing performant, modular transformations at scale.
Have working knowledge of Python, particularly in the context of data orchestration, transformations, and testing.
Understand tradeoffs between different modeling approaches (e.g., Kimball vs. OBT), and can choose appropriate techniques based on data volume and business needs.
Can independently communicate technical concepts to both engineers and non-technical stakeholders.
Think critically about ROI, know when to automate, and understand how to balance long-term quality with short-term delivery.
Benefits
Medical, dental, and vision coverage starting on Day 1
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