Sr. Software Engineering Manager

Salesforce•Bellevue, WA

2d•$172,500 - $260,100•Hybrid

About The Position

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. The Experience: Salesforce Data Cloud powers the intelligence layer of Customer 360 by unifying data from every system, channel, and interaction to build a real-time customer profile. The Streaming Connectors team enables customers to ingest high-volume, real-time data from cloud data warehouses, messaging systems, web and mobile applications, and enterprise applications into Data Cloud with low latency, high reliability, and enterprise-grade security. We are looking for an experienced and technically strong Senior Engineering Manager to lead the Streaming Data Platform team. In this role, you will drive the development of scalable streaming ingestion services, customer-facing connectors, and platform capabilities that power billions of events every day. You will lead a team of talented engineers while partnering closely with Product Management, Architecture, Infrastructure, and Customer Engineering to build highly available, cloud-native services that delight our customers.

Requirements

10+ years of software engineering experience building large-scale distributed systems.
5+ years of engineering management experience leading high-performing software engineering teams.
Strong experience building and operating cloud-native distributed systems in production.
Hands-on experience with streaming technologies such as Apache Kafka, Apache Spark Structured Streaming, or similar real-time processing frameworks.
Experience designing scalable ingestion pipelines, event-driven architectures, or data integration platforms.
Strong programming skills in Java, Scala, or Python.
Experience building services on Kubernetes and public cloud platforms such as AWS, Azure, or GCP.
Understanding of distributed systems concepts including scalability, fault tolerance, consistency, partitioning, checkpointing, and resiliency.
Experience with modern observability platforms.
Proven ability to balance customer delivery, platform investments, operational excellence, and engineering velocity.
Excellent communication and collaboration skills with experience partnering across Product, Architecture, Infrastructure, and Customer Success organizations.
BS/MS in Computer Science or a related technical discipline, or equivalent practical experience.

Responsibilities

Lead and grow a high-performing engineering team responsible for building and operating Data Cloud's real-time streaming connectors and ingestion platform.
Define and execute the technical roadmap for customer-facing streaming integrations across cloud data warehouses, messaging platforms, and SaaS applications.
Drive the architecture and delivery of highly scalable, low-latency distributed systems capable of processing billions of events with high availability and operational excellence.
Partner closely with Product Management, Architecture, Security, and Infrastructure teams to prioritize customer needs and deliver innovative platform capabilities.
Champion engineering excellence through design reviews, code quality, testing, automation, and operational best practices.
Drive improvements in reliability, scalability, performance, and cost efficiency across streaming services and distributed processing pipelines.
Own production health by improving observability, monitoring, incident response, and operational readiness for business-critical services.
Foster a culture of ownership, continuous learning, and technical innovation while mentoring engineers and developing future technical leaders.
Collaborate across the Data Cloud organization to define best practices for streaming architectures, distributed systems, and cloud-native application development.