Director, Engineering Operations

Redis

1d•$175,000 - $250,000

About The Position

The Director, Engineering Operations is a highly visible, deeply technical operator within the CTO organization. This role builds and evolves the organization’s operating system while steering AI transformation across engineering and cloud infrastructure operations. Working across Engineering, Cloud Operations, and product partners, the Director improves delivery confidence, surfaces technical risks early, advances AI-enabled workflows, and helps leaders focus on long-term strategy and architecture. The role establishes effective operating mechanisms without adding unnecessary bureaucracy. It brings clarity to execution, commitments, ownership, and technical decisions, and uses data, automation, analytics, and AI-assisted workflows to improve operating leverage. The Director also provides structure for ambiguous, high-risk, or cross-functional initiatives and supports organization-wide planning and strategic priorities.

Requirements

Deep technical background in software engineering, distributed systems, or cloud infrastructure operations, alongside experience in strategic leadership or business operations.
Demonstrated ability to turn ambiguous, cross-functional problems into clear decisions, ownership, operating mechanisms, and measurable outcomes.
Experience designing and running operating cadences, business reviews, planning processes, leadership forums, or comparable execution-improvement mechanisms.
Ability to influence without formal authority and build trust across geographies, functions, and organizational levels.
Comfort working in a fast-changing environment with incomplete information and competing priorities.

Nice To Haves

Experience supporting a CTO, VP of Engineering, or comparable technology executive in a high-growth or transformation environment.
Experience with annual planning, portfolio management, headcount planning, financial modeling, or investment reviews.
Experience supporting IPO readiness, M&A diligence, organizational transformation, or other enterprise-scale change.
Experience building operating standards, metrics, dashboards, or lightweight automation that materially improves decision quality.
Hands-on experience designing or deploying AI tooling, LLM integrations, AI-assisted coding, or automated operations platforms in large-scale engineering or cloud environments.

Responsibilities

Design, run, and continuously improve the CTO organization’s operating cadence, including quarterly business reviews, initiative and program reviews, goal-setting cycles, leadership forums, and internal communications.
Establish reusable standards for agendas, pre-reads, synthesis, status, risk and confidence language, ownership, dependencies, decision capture, and follow-through.
Turn fragmented execution signals into concise, decision-ready narratives for the CTO and Engineering leadership.
Ensure leadership forums produce decisions and tradeoffs rather than merely status updates.
Make Engineering priorities, commitments, dependencies, and risks visible across the company.
Improve the quality and timeliness of early risk detection and escalation.
Combine data and qualitative signals to distinguish genuine progress from activity or optimism.
Partner with the CTO, Engineering leaders, Finance, People, Product, and other functions to align strategy, priorities, capacity, headcount, funding, and operating norms.
Support workforce and headcount planning, financial and investment alignment, and organizational planning with analysis and decision materials.
Improve cross-geography and cross-functional operating norms so expectations are explicit.
Structure ambiguous, cross-cutting, or high-risk initiatives that lack a natural program home.
Support high-impact priorities such as major transformations, IPO readiness, and M&A diligence requiring CTO-level coordination.
Determine when to remain involved, escalate, or exit strategic initiatives so Engineering Operations does not become a permanent dependency.
Institutionalize good judgment through reusable systems, templates, and standards.
Use automation, analytics, and AI-assisted workflows to improve signal quality and operating leverage.
Communicate operational changes and build adoption through clarity, usefulness, and trust.
Architect and execute initiatives integrating AI into software development lifecycles, CI/CD pipelines, and cloud operations to accelerate innovation and developer efficiency.
Establish frameworks and benchmarks to evaluate AI-tool adoption, effectiveness, and impact across engineering and cloud infrastructure teams.
Lead cross-functional programs for automated observability, AI-driven incident management, and intelligent cloud resource optimization.