Senior Big Data Platform & Distributed Systems Engineer

Description About the Teams: By applying to this role, you will be considered for two mission-critical teams at the heart of our data strategy. Both teams operate at extreme scale, leveraging Public Cloud (AWS/GCP) and Kubernetes to power the next generation of data-driven intelligence. Pillar 1: Data Engineering & Observability Build and own large-scale data pipelines and observability systems that power metrics, logging, and real-time insights across services. This role focuses on designing reliable telemetry pipelines, improving monitoring and alerting, and ensuring data quality and system visibility at scale. Ideal candidates have strong distributed systems fundamentals, backend development experience (Java or similar), and experience operating high-throughput data or monitoring platforms in cloud or hybrid environments. Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code. Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale. Pillar 2: Data Cloud. Big Data Compute platform team. Owns the compute infrastructure that powers large-scale Spark workloads. The team focuses on optimizing core Spark performance, solving distributed systems challenges, and building 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 Required Skills: 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. Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code. Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale. 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 Desired Skills 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. Benefits & Perks Check out our benefits site which explains our various benefits, including wellbeing reimbursement, generous parental leave, adoption assistance, fertility benefits, and more. Salesforce Information Check out our Salesforce Engineering Site . *IN SCHOOL OR GRADUATED WITHIN THE LAST 12 MONTHS? PLEASE VISIT FUTURE FORCE FOR OPPORTUNITIES * #J-18808-Ljbffr