Principal Data Engineer

Upside•Washington, DC

1d•$215,000 - $250,000•Remote

About The Position

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 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 a Principal Data Engineer to be the technical backbone of our Analytics Engineering team. You'll architect and evolve the data platform that powers Upside's most critical product and business decisions: setting patterns, leading cross-functional workstreams, and raising the bar for how data moves through the company. This is a role for someone who wants real ownership. Our team is small and intentional, our scope is large (~2,000 tables, 100 pipelines), and the work directly shapes product features, ML models, and business outcomes. If you do your best work when the problem is ambiguous and the impact is visible, this is the role for you.

Requirements

8+ years in data or analytics engineering with a track record of owning complex, business-critical systems end to end
Hands-on experience with AWS; deployments, workload management, cloud infrastructure and the modern data stack (Snowflake, dbt, Dagster, Databricks, Terraform)
A track record of leading platform migrations, deprecations, or upgrades across shared systems; balancing technical risk, continuity, and long-term maintainability
Ability to design secure, reusable patterns for data ingestion, access control, and platform automation and comfort partnering with Infrastructure, Security, and Governance stakeholders
Experience with DevOps practices (CI/CD for data), data governance, or FinOps
Ability to break down ambiguous, cross-functional problems and lead implementation from design to deployment
Clear communication across all audiences; engineers, analysts, product managers, and business leaders
A genuine drive to mentor others, set standards, and leave every system better than you found it

Nice To Haves

Experience supporting ML workflows; building features or monitoring model inputs and outputs
Background in a fast-growing startup or on platform-style teams that serve internal customers

Responsibilities

Lead platform modernization; deprecate legacy workflows, migrate pipelines to scalable patterns, and sharpen our CI/CD and developer experience
Drive infrastructure and FinOps initiatives across Snowflake, Dagster, and dbt; reducing cost, improving governance, and increasing maintainability
Own platform evolution projects like making data consumable by agentic tools and workflows, and improving orchestration across the analytics stack
Design and deliver complex, domain-critical data products used by analysts, data scientists, and product teams to ship features and drive strategy
Architect reusable, extensible patterns for modeling, orchestration, and transformation; balancing flexibility, cost, and long-term maintainability
Lead technical planning across cross-functional teams, breaking down large data initiatives into scoped, sequenced workstreams
Mentor engineers, build internal documentation and tooling, and raise the overall standard for analytics engineering at Upside
Partner with engineering, product, data science, and business stakeholders to deliver end-to-end solutions
Represent Data Engineering in design forums and shape the future roadmap for data at Upside