Engineering Manager - Data Engineering

Location

Austin, Chicago, DC, NYC

Full time

Hybrid

Engineering

• $210K – $260K

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.

As engineering leaders at Upside, we strive to gather the right information from those around us and take the right calculated risks. In doing so, we hope to help teams quickly make key decisions that will ship high quality software with the most delightful experience possible to our users.

We’re looking for a Senior Engineering Manager to lead the Infrastructure and Data Platform teams, two foundational groups responsible for Upside’s core platforms. These teams provide the cloud, data, and analytics capabilities that power every application, insight, and decision across the company. This leader will combine technical competence with people-first leadership to drive operational excellence, foster growth, and scale the systems that underpin our product and internal platforms.

This role will also help close a strategic gap in our technical organization by guiding the evolution of MLOps practices that support data science and machine learning innovation.

• Create a safe, collaborative team environment. You name problems and invite open discussion, which motivates your team to do their best work.

• Operate with a first-team mindset. You lean in when important areas need help, regardless of whether it's “your” domain, and prioritize the success of the whole organization.

• Raise the bar. You identify and influence improvements in quality, performance, speed of execution, and engineering standards.

• Grow talent. You coach your engineers, recognize their achievements, and help them develop skills that will benefit them and Upside for years to come.

• Set clarity and empower autonomy. You ensure expectations and direction are clearly articulated so that individuals and teams can operate with independence, confidence, and creativity.

In addition to the leadership responsibilities above, you will:

• Champion system-level quality by identifying and addressing gaps in reliability, scalability, security, and performance across infrastructure and analytics systems.

• Ensure the cloud platform is secure, cost-efficient, and production-ready, and that the data stack enables self-service, repeatable, and trusted workflows.

• Shape technical direction across Dev Ops, Data Ops, and MLOps domains, and drive strategic upgrades such as orchestrator migrations, cloud modernization, and analytics workflow enablement.

• Oversee large-scale programs that span infrastructure, data, and analytics domains, aligning execution with business priorities and cross-team dependencies.

• Establish and monitor key operational metrics (e.g., platform availability, cost per transaction, data pipeline reliability, MLOps readiness).

• Act as the primary point of contact for internal partners (e.g. Product, Product Engineering, Security, Data Science, and Retail Go To Market), ensuring platform capabilities support evolving business needs.

• Represent the infrastructure and analytics engineering teams in cross-functional planning, technical councils, and long-term roadmap discussions.

• Have 5+ years of engineering management experience, ideally across platform, data, or infrastructure domains.

• Thrive in environments where you’re asked to scale teams, elevate systems, and solve ambiguity in measure.

• Have familiarity with cloud infrastructure (AWS preferred), modern data platforms (e.g.,…