Upside is a nationally scalable housing stability platform that partners with health plans and employers to identify and stabilize people experiencing housing instability.
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. 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. About the role: We're looking for a Staff Data Engineer to serve as a technical leader on the Data Engineering team. In this role, you'll drive the design and implementation of foundational data products and analytics platform capabilities that power Upside’s most critical product and business use cases. You’ll lead cross-functional workstreams, shape patterns and architecture across teams, and elevate the overall quality and impact of data work at Upside. This role is ideal for someone who enjoys deep technical problem-solving, cares about quality and long-term maintainability, and is motivated by helping others work more effectively with data. Here are some ways we have seen data & analytics engineers drive impact at Upside: - Lead platform modernization efforts across the analytics ecosystem, such as deprecating legacy workflows and tooling, migrating pipelines to more scalable patterns, and improving the infrastructure, CI/CD, and developer experience - Drive high-leverage infrastructure and FinOPs initiatives across systems like Snowflake, Dagster, and dbt, reducing cost, improving governance, and increasing the scalability and maintainability of Upside’s data platform - Own platform evolution projects such as making data more consumable by agentic tools and workflows, or improving orchestration tooling for analytics workflows. - Design and deliver highly complex, domain-critical data products used by analysts, data scientists, and product teams to unlock new product features, ML models, and strategic decisions. - Architect scalable, extensible patterns for modeling, orchestration, and data transformation, balancing flexibility, reusability, and cost-efficiency. - Lead technical planning and delivery across cross-functional teams, breaking down complex data initiatives into scoped, sequenced workstreams implemented by you and others. - Drive platform adoption and best practices, mentoring other engineers, building internal documentation and tooling, and raising the overall bar for analytics engineering across the company. - Influence upstream and downstream teams, partnering with engineering, product, data science, and business stakeholders to align on requirements and deliver end-to-end solutions. - Represent Data Engineering in technical design forums and contribute to roadmap discussions that shape the future of data at Upside. Why You Should Apply This role is a good fit for you if: - You aren’t afraid to challenge the status quo when it makes the team and business better. You learn from those around you while utilizing data to advocate for informed change. - 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. You bring a mindset of continuous improvement, and know when to invest in observability, automation, or new infrastructure to reduce toil and improve outcomes for the team and end users. - You believe that pulling quality upstream starts with engineering. You champion best practices, encourage early testing and validation, and work closely with peers to build a culture of quality from the ground up. Ideal Qualifications - Have 8+ years of experience in data or analytics engineering, with a track record of owning complex, business-critical data systems end to end. - Have deep experience with the modern data stack (e.g. Snowflake, dbt, Dagster, Databricks), terraform, and cloud infrastructure, and can use those systems to improve performance, reliability, security, and developer experience at scale. - Have a track record of leading platform migrations, deprecations, or upgrades across shared systems, balancing technical risk, operational continuity, and long-term maintainability. - Can design secure, reusable patterns for data ingestion, access control, and platform automation, and are comfortable partnering with Infrastructure, Security, and Governance stakeholders to implement them. - Experience with DevOps practices (e.g., CI/CD for data), data governance, or FinOps (cost-conscious design). - Can break down ambiguous, cross-functional data problems and lead the implementation from design to deployment, collaborating across technical and non-technical teams. - Proactively identify opportunities to improve the analytics platform and are comfortable designing and implementing impactful, reusable solutions. - Communicate clearly across audiences, from engineers and