Technical Lead, eDiscovery Products

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Ryz Labs

WebsiteLinkedInAll Job Openings

11 - 50 employees

Ryz Labs builds startups from the ground-up and helps other startups scale by providing top-tier technical talent solutions.

📋 Description

• Lead engineering on the replatforming of a suite of eDiscovery tools — serving as the primary technical decision-maker on architecture, infrastructure, data modeling, and integrations. • Architect and build migration of an analytics tool from desktop to a modern web-based platform, including ingestion pipelines for email data (Reveal RDM) and mobile data (Cellebrite). • Drive the technical evolution of a document review tool from a platform-hosted experience to a standalone application integrating with Relativity via API, with a path to supporting additional review platforms. • Establish engineering foundations from scratch: CI/CD, testing infrastructure, deployment practices, coding standards, and security posture. • Partner with the Product Manager to sequence work, scope complexity, and surface trade-offs — ensuring architectural decisions serve both near-term delivery and long-term platform goals. • Build and grow the engineering team, hiring and mentoring engineers while cultivating a culture of craft and pragmatism.

🎯 Requirements

• 10+ years of software engineering experience, with meaningful time spent in legal technology, eDiscovery, or adjacent compliance/investigations domains. • Deep, hands-on expertise with Relativity custom development: you have built custom applications, integrations, or workflows on Relativity — you know its data model, APIs, and review workflows inside out. • Proven ability to build from scratch: you have served as a founding or lead engineer on at least one greenfield product, making early architectural decisions that defined it for years. • Strong command of modern web application architecture — you can design and implement a scalable, full-stack application including cloud infrastructure, APIs, and data pipelines. • Experience designing large-scale data ingestion and processing pipelines, including handling schema variability, deduplication, and transformation at scale. • Startup mindset: comfortable with ambiguity, able to move quickly, and skilled at making pragmatic architectural decisions that balance speed with long-term soundness.

🏖️ Benefits

• Fully remote - LATAM