Senior Manager - Software Engineering

Salesforce•San Francisco, CA

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. At Salesforce, trust is our #1 value, and the Salesforce Guardian organization builds the security, resilience, and data governance capabilities that thousands of customers rely on every day. We are looking for a Sr. Engineering Manager to join the team! The Unified Observability team is building on its deep foundation in event data to deliver a single, coherent platform for metrics, logs, and traces across Salesforce. Our charter is to give customers one place to troubleshoot and reason about behavior across multiple services. You'll lead the Unified Observability team as it takes on a major new initiative, with a clear product vision, strong PM and design partnership, and real headroom to shape how the team is built and how it operates. The work is visible, the customer impact is direct, and as the platform grows into a foundational capability across the organization, so does the scope of this role. We're committed to building an inclusive team where engineers from a range of backgrounds and experiences can do their best work — if this sounds like the right next chapter, we'd love to hear from you

Requirements

8+ years of software engineering experience, including 4+ years directly managing engineering teams (people management, not just tech lead).
Track record of shipping complex, cross-team distributed systems and owning delivery outcomes end-to-end.
Comfort with — and enthusiasm for — AI-assisted engineering; experience setting expectations or practices around AI coding tools (e.g., Claude Code, Codex, or similar) on a team you've led.
Strong technical judgment: able to go deep enough in design and code review to ask the right questions, even without writing the majority of the code yourself.
Experience hiring, growing, and managing performance for engineers across a range of seniority levels.
Proven ability to partner with Product and engineering staff to turn ambiguous problems into a clear, sequenced roadmap.
Excellent written and verbal communication; able to represent the team's priorities and tradeoffs to cross-functional and senior stakeholders.
A bias toward measurable customer outcomes, and comfort being accountable for them.

Nice To Haves

Background managing teams in observability, monitoring, telemetry pipelines, SRE/on-call tooling, or incident-response systems.
Familiarity with the OpenTelemetry ecosystem, high-cardinality metrics, distributed tracing, and log aggregation at scale, and telemetry stores such as HBase or ClickHouse.
Experience managing teams that ship data-visualization or dashboarding products.
Experience operating in multi-tenant, distributed systems at Salesforce scale or comparable enterprise scale.
A track record of building inclusive, high-performing engineering cultures through periods of team growth.

Responsibilities

Own the delivery and health of the Unified Observability team: staffing, prioritization, execution rhythm, and quality bar.
Partner closely with Product Management and the engineering team to align cross-functional architectural decisions and translate product strategy into an executable roadmap.
Drive cross-team alignment with partners on data ingestion, query performance, and cost boundaries — representing the team in cross-org planning and dependency negotiation.
Remove blockers and make the tradeoffs (scope, sequencing, risk) that keep delivery on track, escalating and communicating clearly when tradeoffs affect commitments.
Own operational health: on-call practices, incident response, and production reliability for the platform.
Build and safeguard team culture — a collaborative, low-ego environment where engineers do their best work.
Model and champion AI-assisted engineering as a default part of how work gets done
Set expectations and hold the team accountable for using AI coding assistants throughout the development lifecycle: scaffolding, tests, migrations, code review, and production debugging
Coach engineers on judgment — where AI accelerates the work, and where it demands careful human verification
Sponsor and prioritize internal tooling/agents that automate repetitive engineering work (PR triage, on-call runbooks, log analysis, capacity planning)
Create space for the team to share patterns, prompts, and workflows that make everyone faster