Principal Software Engineer, Core Experiences (Remote Eligible)

Core Experiences owns the platform and front end behind Smartsheet's most used work surfaces. These are the sheets, reports, and dashboards millions of people open every day to plan, track, and run their work. Underneath them are petabytes of customer work data, one of the largest and most valuable datasets at the company. As AI agents join human teams, that data has to serve two very different users. People expect an instant, real-time collaborative experience. Agents read, write, and analyze at machine scale. Meeting both means rethinking how sheet data is stored, served, and moved between our operational systems and a world-class lakehouse platform. We hold that work to the highest standards of performance, reliability, and correctness.

As a Principal Software Engineer on Core Views, you will set the technical direction for systems at the heart of Smartsheet. This is a hands-on architect role. You will spot the company's largest technical opportunities before they are obvious, prove them out with prototypes, and build alignment across engineering organizations, including on roadmaps you do not own. You will turn multi-quarter efforts into phased plans with clear milestones and candid risk reporting, then deliver them. You will make the hard trade-offs between latency, consistency, and cost. You will write the architecture decisions and standards other teams build on, and raise the technical bar for engineers across the company. Your first major initiative will reshape how sheet data moves between our transactional stores and our data platform. What comes after is yours to help define.

You will report to our Sr. Director of Engineering located in our Bellevue, WA office, or you may work remotely from anywhere in the US where Smartsheet is a registered employer.

You Will:

• Architect the Sheet Data Platform: Own the end-to-end design for how sheet data is stored, tiered, and served across our transactional databases and our lakehouse platform. That includes data lifecycle and retention strategy, schema evolution, asynchronous access patterns, and consistency guarantees that users and agents can rely on.
• Design for People and Agents at Scale: Shape the services and APIs that serve sheet data to a real-time collaborative experience used by millions and to AI agents that read, write, and analyze at machine scale. Make the hard trade-offs between latency, consistency, and cost, and document them so others can build on them.
• See Around Corners: Identify the largest technical opportunities and risks across Core Views and the platforms it depends on before they become obvious. Build the prototypes that prove them out and turn them into initiatives leadership will invest in.
• Lead High-Stakes Migrations: Plan and execute changes to petabyte-scale production data with zero customer impact. That means phased cutovers, backfill and reconciliation, verification at scale, and a rollback path for every step.
• Own Delivery End to End: Scope multi-quarter efforts into phased plans with clear milestones, track progress across teams, raise risks early and candidly, and ship on time.
• Carry Architecture Across Org Boundaries: Partner with Product, Data Platform, AI Platform, and Infrastructure to align on direction. Influence roadmaps you do not own, and represent Core Views in company-level platform and vendor decisions.
• Set the Standard: Bring industry best practices into Smartsheet engineering. Write the architecture decisions, RFCs, and standards other teams build against, and lead the design reviews that hold them to that bar.
• Lead on AI-Driven Engineering: Define the patterns and guardrails for integrating AI capabilities into complex multi-system architectures and into how our engineers build software. Measure the impact so successful practices spread across groups.
• Stay Hands-On: Solve complex problems across multiple systems with simple, high-performing code in Java on AWS and Kubernetes. Do deep code reviews across product areas, and mentor senior and staff engineers on distributed systems and data-intensive design.
• Own Production at the Deepest Level: Participate in on-call and lead the most complex incidents to resolution. Dig into the internals of databases and infrastructure to find performance bottlenecks, and drive the root-cause work that makes the platform more resilient.

You Have:

• 10+ years building highly scalable, highly available, low-latency systems, including several years operating at Principal or Staff level. You write production-quality Java or Kotlin and can work comfortably across the stack.
• Asynchronous Distributed Systems: You've designed and run async, event-driven systems on AWS or comparable cloud infrastructure. You know firsthand where they break: ordering, idempotency, back-pressure, partial failure, and consistency across service boundaries.
• Large Data in Production: You've owned systems backed by very large relational or distributed datastores. You've tuned them, scaled them, outgrown them, and moved data off them without customers noticing.
• Ambitious Projects, Shipped: You've taken a fuzzy, high-stakes technical problem from first prototype to production across multiple teams. You can show the phased plan, the risks you called early, and the outcome.
• Direction Others Adopted: You've written the design docs, RFCs, or standards that changed how an engineering organization builds, and they held up after you moved on.
• LLMs in Real Products: You've built LLM-enriched workflows that delivered measurable value to SaaS customers, and you have grounded opinions on what AI does and doesn't change about building software.
• A degree in Computer Science, Engineering, or a related field, or equivalent practical industry experience.
• Legally eligible to work in the U.S. on an ongoing basis.

Nice to Have

• Databricks (Delta Lake, Unity Catalog, Spark) or comparable lakehouse experience such as Snowflake or Apache Iceberg. If you haven't used it but learn fast, we still want to hear from you.
• Streaming platforms such as Kafka or Kinesis.
• Building data access for AI agents, such as MCP servers or agent-facing APIs.
• Real-time collaborative applications.