About Onit
We're redefining the future of legal operations through the power of AI. Our cutting-edge platform streamlines enterprise legal management, matter management, spend management and contract lifecycle processes, transforming manual workflows into intelligent, automated solutions.
We’re a team of innovators using AI at the core to help legal departments become faster, smarter, and more strategic. As we continue to grow and expand the capabilities of our new AI-centric platform, we’re looking for bold thinkers and builders who are excited to shape the next chapter of legal tech.
If you're energized by meaningful work, love solving complex problems, and want to help modernize how legal teams operate, we’d love to meet you.
Position Summary
The Senior Software Engineer - Data designs and builds the data-intensive services, pipelines, and APIs that power analytics and AI features across Onit's legal operations platform, spanning enterprise legal management, matter management, spend management, and contract lifecycle management. This engineering-focused role applies strong software engineering practices to data: clean, tested, production-grade code, well-designed data models, and reliable, performant systems. Working with architects, data scientists, product managers, and fellow engineers, the Senior Software Engineer - Data owns features end to end, sets the technical approach for their area, and mentors less experienced engineers.
Key Responsibilities
• Data Services & Pipelines: Design, build, and own production-grade data pipelines, services, and APIs that ingest, transform, and serve data from multiple source systems to product features.
• Data Modelling: Design relational and analytical data models and schemas that keep figures consistent and performant across product surfaces, in line with the architecture defined by the Data Architect.
• Performance & Scalability: Profile and optimise queries, services, and pipelines for performance, scalability, and cost on large datasets.
• Data Quality & Reliability: Build automated tests, data validation, monitoring, and alerting so data issues are caught early, and lead troubleshooting and root-cause analysis of production issues.
• Security & Multi-Tenancy: Implement multi-tenant data isolation, access controls, and privacy safeguards such as anonymisation and aggregation thresholds.
• Engineering Excellence: Set the technical approach for features in your area, lead design and code reviews, and contribute to engineering standards, CI/CD, and documentation.
• Mentoring & Collaboration: Mentor Software Engineers, and partner with product, design, data science, and QA to deliver features from concept to production.
Required Skills
• 5+ years of professional software engineering experience, including significant hands-on work building data-intensive applications, services, or pipelines in production.
• Strong proficiency in Python (or a comparable language such as Java, C#, or Ruby), with solid software engineering fundamentals: design patterns, testing, and clean, maintainable code.
• Advanced SQL and relational data modelling, including query optimisation and performance tuning on large datasets (e.g., PostgreSQL, SQL Server, or MySQL); exposure to analytical or columnar data stores.
• Experience designing and building REST APIs and backend services that expose data to product features.
• Experience building ETL/ELT pipelines, including orchestration, incremental processing, data validation, and error handling.
• Experience with cloud platforms (AWS preferred), Git-based workflows, CI/CD, and containerisation (Docker).
• Working proficiency with AI coding tools (e.g., Claude, GitHub Copilot) to accelerate development, debugging, and test writing, with the judgment to review and validate AI-generated code before it ships.
• Clear communication and the ability to mentor engineers, explain technical trade-offs, and collaborate across product and data teams.
Preferred Qualifications
• Experience with distributed data processing (e.g., Spark) or workflow orchestration tools (e.g., Airflow).
• Experience with multi-tenant SaaS systems and data isolation.
• Experience building analytics, reporting, or benchmarking features in an existing transactional product.
• Background in enterprise B2B SaaS or the legal technology domain.
