Principal Software Engineer, Core Experiences (Remote Eligible)

Smartsheet empowers teams to manage work seamlessly and is uniting human teams with AI agents to automate tasks and uncover insights. The Principal Software Engineer will set the technical direction for Core Experiences systems, architect the sheet data platform, lead large-scale migrations, influence cross-organizational roadmaps, and deliver high-performance distributed systems.

Responsibilities:

• 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
• 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
• 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
• 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
• Scope multi-quarter efforts into phased plans with clear milestones, track progress across teams, raise risks early and candidly, and ship on time
• 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
• 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
• 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
• 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
• 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

Requirements:

• 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
• 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
• 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
• 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
• You've written the design docs, RFCs, or standards that changed how an engineering organization builds, and they held up after you moved on
• 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
• 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