Manager, eDiscovery Operations

About The Team

Legal Operations builds the infrastructure that powers Door Dash’s Legal function. We design the workflows, systems, and data foundations that transform complex legal work into structured insight — enabling Legal to operate at the speed of the business. Our team owns the core platforms and scalable processes that support a growing global organization as data volume and regulatory complexity increase.

We view high-quality, well-structured data as a strategic asset, and we intentionally build the technology and workflows that generate it by design. If you’re a builder who thrives at the intersection of law, technology, and operational transformation, you’ll find a home here.

We are hiring a Manager, eDiscovery Operations to architect and evolve the operational infrastructure that supports Door Dash’s litigation and legal response efforts. This role goes beyond managing matters — you will design and own the operating model for how data moves from enterprise systems into defensible legal review.

This is a high-autonomy individual contributor role with ownership over discovery infrastructure and oversight of contractors and external vendor teams.

Success in this role is measured by the scalability, reliability, and defensibility of the discovery systems you design, as well as the expertise you bring to complex, high‑impact matters. You are a builder first–focused on creating automated, repeatable processes that treat discovery as a scalable technical product.

• Engineer and maintain end-to-end data pipelines and delivery models that embed defensibility by design, ensuring the eDiscovery lifecycle scales seamlessly with Door Dash’s increasing data complexity.
• Lead a diverse ecosystem of contractors and vendors to execute daily operations across legal holds, preservation, and production on complex matters.
• Serve as the primary eDiscovery consultant to Litigation, Privacy, Employment, IT and Security teams, translating technology and requirements into executable processes.
• Provide expert guidance on data minimization and early case assessment strategies to reduce review volume, mitigate risk, and control costs.
• Own performance, capacity planning, and quality standards across our vendor ecosystem, treating external partners as an extension of our technical infrastructure.
• Define and operationalize technical standards and best practices for discovery workflows, including documentation, automation, and scalable training.
• Drive continuous improvement through automation, analytics, and AI‑enabled review capabilities to reduce manual overhead and optimize legal spend.

• You have 6–8+ years of experience in eDiscovery, with at least 2 years in a systems‒building or program management capacity.
• You bring hands‒on experience across modern review platforms (e.g., DISCO, Everlaw, Relativity One), including oversight of vendor‒managed environments.
• You are a power user of foundational AI models (ChatGPT, Gemini) to improve individual productivity, and have a proven track record of leading cross‑functional teams to apply TAR, CAL, and LLM‒based workflows to solve complex, high‑volume data challenges.
• You are comfortable navigating enterprise tools (Jira, Confluence) to manage technical roadmaps and productize legal workflows.
• You treat eDiscovery as a financial and technical portfolio, using e‑billing and data metrics to forecast and project spend.
• You have deep knowledge of cloud storage (AWS), secure data transfer, and chain‑of‑custody protocols within enterprise environments.
• You have excellent written and verbal communication skills with a focus on detail, organization, and responsiveness.
• You are flexible and adaptable to changing priorities, scope, and technology.

We expect this position to be filled by 5/15/2026.

The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job–related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work…