Senior Software Engineer - Data

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.

• 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.

• 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.

• 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.