Senior Analytics Engineer

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Meet Upside We created Upside to transform brick-and-mortar commerce. Our technology uses the sophistication of online retail—profit measurement, attribution, and incrementality—to provide users with more value on their everyday purchases and brick-and-mortar businesses with new, profitable customers. We’ve helped millions of users earn 2 to 3 times more cashback than any other product, and hundreds of thousands of brick-and-mortar businesses earn measurable profit.

Billions of dollars in commerce run through the Upside platform every year, and that value goes directly back to our retailer partners, the consumers they serve, and important sustainability initiatives.

We're looking for an Analytics Engineer to join the team responsible for Upside’s core data platform. You’ll play a key role in building trusted data products that serve both internal teams and customer-facing features, while also improving the analytics platform and enabling others to do their best data work.

This is a great opportunity for someone who’s confident working with large datasets and complex business logic, thrives in a fast-moving environment, and wants to grow as a technical contributor while collaborating closely with product managers, analysts, data scientists, and engineers across the company.

• 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.

• You aren’t afraid to challenge the status quo when it makes the team and business better.
• You thrive at the intersection of systems and storytelling, not only building robust solutions but also communicating their purpose, impact and rationale, so teams can experiment, iterate, and act confidently.
• You care about building resilient systems that scale, with a mindset of continuous improvement and investing in observability, automation, or new infrastructure to reduce toil.
• You believe that pulling quality upstream starts with engineering, champion best practices, encourage early testing and validation, and work closely with peers to build a culture of quality from the ground up.

• Have 2+ years of experience 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 data orchestration, transformations, and testing.
• Understand tradeoffs between different modeling approaches 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.

• Experience supporting machine learning workflows, such as building features or monitoring model inputs and outputs.
• Familiarity with Dev Ops practices (CI/CD for data), data governance, or Fin Ops (cost‑conscious design).
• Experience working in a fast‑growing startup environment or on platform‑style teams that serve internal customers.

We want our engineers to have the time and support to grow in their…