Manager, Software Engineering

Relativity is seeking an experienced Software Engineering Manager to lead teams responsible for core data processing services. The role involves overseeing systems that transform large volumes of data into structured information, ensuring speed, reliability, and accuracy for customers navigating complex legal matters.

Responsibilities:

• Oversee design, development, and operation of the Processing Discover services with a focus on reliability, scalability, and security
• Drive modernization initiatives, including migration to microservices and adoption of cloud-native technologies
• Set technical direction, drive engineering standards, and establish operational processes
• Contribute to the product roadmap and use it to guide team priorities, execution, and backlog decisions
• Mentor and develop engineering talent, fostering a strong operational mindset and domain expertise

Requirements:

• Experience managing engineering teams responsible for mission-critical, high-throughput, low-latency cloud services
• Solid foundational knowledge in software engineering fundamentals, including system design, object-oriented programming, data structures, algorithms, and microservice architectures
• Experience building scalable, fault-tolerant distributed systems in a public cloud environment
• Strong operational skills with a focus on automation, observability, metrics driven decision making, and rapid incident responsiveness
• Ability to quickly learn and lead in a complex, domain-specific technical environment
• Experience leading teams using Scrum or other agile development frameworks
• Budget Management
• Engineering Management
• Innovation
• Leadership
• Performance Management (PM)
• Process Improvements
• Project Management
• Quality Assurance (QA)
• Risk Management
• Stakeholder Management
• Track record of modernizing legacy or monolithic architectures to microservices
• Experience managing remote teams or distributed team members
• Familiarity with cloud computing, big data, and data engineering technologies (e.g., Temporal, Spark, Azure Data Lake Storage)
• Familiarity with CI/CD pipelines and DevOps practices