Senior Engineering Manager, Non-Linear Productivity

About the role

Senior Engineering Managers at GitLab own outcomes for a defined engineering domain, build and grow the team that delivers them, and are trusted to operate with significant autonomy against a clear mandate.

This Senior Engineering Manager role focuses on driving non-linear productivity by building and leading a small, hand-picked team of engineers who find and fix the highest-leverage friction points in GitLab's own SDLC. Rather than incremental team management, this is a 0 to 1 team-building mandate. You will personally recruit 4 exceptional engineers, define how they work, and drive them through a root-cause, shift-left approach to engineering productivity, measured in system-level metrics (MRPM, pipeline success rate, pipeline latency), not story points or activity.

You will act as a bridge between the pilot squad, engineering managers whose teams are affected by friction points, and leadership, ensuring the team's fixes deliver durable, measurable value before any of it is proposed as a permanent function.

This role reports to a VP-level engineering leader.

What you'll do

Vision & Strategy

• Define and continuously refine the operating model for the non-linear productivity pilot: what "friction" means, how it's diagnosed, and how fixes are prioritized against the three system-level metrics.
• Identify and prioritize non-linear productivity opportunities (step-change fixes rather than incremental gains) across the SDLC, including flaky tests, review latency, pipeline reliability, tooling gaps, and context loss for both human and agentic contributors.
• Translate an intentionally broad, ambiguous mandate ("find the friction, fix it at the root") into a concrete, time-boxed roadmap with defined milestones for a 3-month pilot window.

Team Building & Leadership

• Personally source, evaluate, and select 4 exceptional engineers for the pilot squad. You own this hiring bar directly rather than delegating it to a pipeline.


• Negotiate allocation models (100% vs. partial/dual-hat) with each engineer's current EM, balancing team commitments against the pilot's needs.


• Set the technical direction and working norms for a small, fast-moving team: how work is scoped, how decisions get made, and how progress gets reported.


• Mentor the engineers on this team in root-cause diagnosis and shift-left thinking, raising the bar for rigor versus reaching for the nearest patch.

Deep Technical Exploration & Experimentation

• Lead hands-on investigation to instrument and baseline the three top-level metrics (MRPM, pipeline success rate, pipeline latency) so the org has an honest starting point before any fix is proposed.


• Diagnose the highest-leverage friction points dragging each metric down, and validate hypotheses with real data before committing team time to a fix.


• Design and implement fixes that compound, including shift-left quality changes, tooling investment, and better context and interfaces for both humans and agents, rather than one-off patches.


• Stay hands-on enough to review architecture, dig into pipeline internals, and unblock the team on hard technical problems directly.

Internal Adoption & Non-Linear Productivity

• Own a small set of high-impact friction areas as pathfinders, driving each from diagnosis through fix to measurable metric movement.


• Work directly with affected engineering teams to ensure fixes are trusted, adopted, and don't quietly get reverted or worked around.


• Track and report metric movement honestly, including when a bet didn't pay off.


• Capture and codify reusable patterns and playbooks so that fixes validated by this team can be adopted org-wide with minimal friction.

Operations, Risk & Enablement

• Set a cadence for reporting impact back to EM peers and leadership, framed around metric movement rather than activity.


• Ensure the pilot's fixes degrade gracefully and don't introduce new reliability risk into CI/CD systems that other teams depend on.


• Help define what "graduation" from pilot to permanent function looks like, and build the case (or the honest counter-case) based on results.

What you'll bring

General

• Track record leading small, high-autonomy engineering teams, ideally in developer productivity, CI/CD, platform, or infrastructure reliability, measured on system-level outcomes rather than feature delivery.


• Deep comfort with CI/CD and build systems. You understand why pipelines flake, why they're slow, and what actually moves success-rate and latency numbers versus what just feels like progress.


• Demonstrated root-cause instinct and a bar for talent over headcount: you'd rather run understaffed than fill a seat with someone good-enough.


• Comfort operating under metrics-driven accountability, including publicly baselining a number and reporting honestly against it.


• Strong executive-level communication. This pilot has direct visibility to engineering leadership.

Nice to have

• Experience with agentic or AI-assisted development workflows, and a view on where human/agent friction shows up differently than pure human friction.


• Prior experience running a defined-window pilot or 0 to 1 initiative with explicit success criteria.


• Familiarity with GitLab's own CI/CD internals, or equivalent depth on another large-scale build/test system.