Description

Salesforce pioneered a new technology model with our cloud platform, a new pay-as-you-go business model focused on customer success, and a new integrated corporate philanthropy model, called the 1-1-1 model, by which we donate 1% of our equity, 1% of our employee time, and 1% of our product to improve communities around the world.

This vision has fueled our incredible growth, made us the global leader in CRM, defined the era of enterprise cloud computing, and inspired a new philanthropic model for all companies to follow. Today, Salesforce's Customer Success Platform provides groundbreaking cloud services for sales, service, marketing, community, analytics, apps and the Internet of Things.

Thanks to our dedicated employees, partners, and the customers we serve, Salesforce is the fastest growing top 10 software company in the world and has been ranked as one of the world's most innovative companies by Forbes for five years in a row. Salesforce empowers companies to connect with their customers in a whole new way, run their business from their phones, create one-to-one customer journeys and make smarter, more predictive decisions from anywhere in real-time.

About Salesforce Data Cloud:

Salesforce Data Cloud powers the intelligence layer of Customer 360, bringing together data from every system, channel, and interaction to build a unified, real-time customer profile. The Fabric engineering team builds the foundational data and compute platform that enables scalable, reliable, and AI-driven data processing at massive scale.

We are looking for an experienced Lead Member of Technical Staff (LMTS) with deep expertise in Big Data and Distributed Compute frameworks such as Apache Spark, Flink, and Ray. In this role, you will architect and build systems that power next-generation data processing, AI workloads, and real-time analytics across billions of records.

Your Impact:

As a key technical lead within the Fabric team, you will:

• Design and build scalable, high-performance distributed compute systems using Spark, Flink, and Ray.

• Architect data processing frameworks that power batch, streaming, and AI inference workloads in Salesforce Data Cloud.

• Lead end-to-end technical design, performance optimization, and scalability efforts for Compute Platform services.

• Collaborate cross-functionally with teams to deliver functionalities.

• Drive innovation in real-time data streaming, low-latency processing, and vectorized computation.

• Champion reliability, cost efficiency, and observability for distributed data workloads on Kubernetes (EKS).

• Mentor and guide engineers through design reviews, architectural decisions, and best practices.

What You’ll Bring:

• 8+ years of experience building large-scale, distributed data systems.

• Deep expertise in Apache Spark, Flink, or Ray, including performance tuning and production deployment.

• Strong programming skills in Java, Scala, or Python.

• Proven understanding of distributed computing concepts such as task scheduling, checkpointing, fault tolerance, and state management.

• Experience deploying and optimizing big data workloads on Kubernetes (EKS).

• Hands-on experience with data formats (Parquet, ORC, Delta) and streaming frameworks.

• Ability to solve complex challenges involving scale, cost, and reliability trade-offs.

• Familiarity with observability and DevOps tools (Prometheus, Grafana, OpenTelemetry).

• Strong collaboration and leadership skills with a track record of technical mentorship.

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.

For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.