Staff Data Engineer

About the role

You will lead the design and implementation of foundational data products and analytics platform capabilities that directly power Upside's most critical product and business use cases. You will serve as a technical leader on the Data Engineering team, owning complex initiatives from discovery through productionization. In this role, you will shape platform architecture and patterns across multiple teams while elevating the quality and impact of data work across the organization. You will drive cross-functional workstreams that modernize the analytics ecosystem and improve the developer experience. This role is ideal for someone who enjoys deep technical problem-solving and is motivated by enabling others to work more effectively with data. You will represent Data Engineering in technical design forums and contribute to roadmap discussions that define the future of data at Upside.

Key facts

What you'll do

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.

Champion quality and long-term maintainability by establishing standards, observability, and testing practices that reduce toil and improve outcomes for both engineers and end users.

Evaluate emerging tools and patterns, running experiments that demonstrate scalability, reliability, and cost impact before broader rollout.

Collaborate closely with data product managers and business stakeholders to translate requirements into robust data models and pipelines that support new functionality.

Ensure data reliability, performance, and security by implementing best practices for access control, monitoring, and alerting across critical data assets.

Continuously assess the operational burden of data workflows and drive automation to reduce manual effort and improve team efficiency.

Requirements

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 a proven ability to learn new technologies quickly.

Have experience designing and implementing data models in dimensional and entity-relationship styles, and know when each approach is appropriate.

Have experience building and maintaining robust data ingestion, transformation, and consumption pipelines that support high-volume, low-latency use cases.

Have experience with SQL-heavy environments, writing complex queries and optimizing for performance, cost, and maintainability.

Have experience with containerization and infrastructure as code, using tools such as Docker and Terraform to manage environments and deployments.

Have experience working in a CI/CD environment for data pipelines, including version control, testing, and deployment practices.

Have strong communication skills and the ability to influence without authority, partnering with cross-functional stakeholders to align on goals and tradeoffs.