Principal)

Salesforce•Bellevue, WA

19d•$117,200 - $313,700•Hybrid

About The Position

Salesforce is seeking experienced engineers for two critical teams focused on data strategy and operating at extreme scale using Public Cloud (AWS/GCP) and Kubernetes. The role involves building and owning large-scale data pipelines, observability systems, and the compute infrastructure for Spark workloads. A key aspect of the role is integrating AI agents into human workflows to drive efficiency and innovation, and critically evaluating both human and AI-generated code. Candidates will leverage modern engineering practices and AI as a core part of their development workflow to deliver secure, optimized, and high-quality code. The position is open to Mid/Senior/Lead/Principal levels.

Requirements

Strong understanding of distributed systems design, including scalability, fault tolerance, and consistency trade-offs in large-scale data platforms.
Experience designing and operating large-scale data pipelines, ETL workflows, or streaming data systems.
Experience with big data and data platform technologies such as Spark, Flink, Kafka, Trino, HBase, or similar.
Experience operating data platforms or infrastructure services at enterprise scale.
Experience building or operating observability systems, telemetry pipelines, or monitoring platforms.
Experience using metrics, logging, and telemetry to drive operational excellence.
Backend development experience (Java or similar).
Experience operating high-throughput data or monitoring platforms in cloud or hybrid environments.

Nice To Haves

A demonstrated, genuine AI-first approach to engineering.
Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows.
Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.

Responsibilities

Build and own large-scale data pipelines and observability systems that power metrics, logging, and real-time insights across services.
Design reliable telemetry pipelines, improve monitoring and alerting, and ensure data quality and system visibility at scale.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow.
Own the compute infrastructure that powers large-scale Spark workloads.
Optimize core Spark performance and solve distributed systems challenges.
Build scalable AI infrastructure, including exploring efficient ways to run smaller language models.
Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance.