Staff Software Engineer - Reporting, Data Platform & Observability

Strength in Trust OneTrust’s mission is to enable innovation through the responsible use of data and AI. We believe that ensuring data is trusted shouldn’t slow teams down—it should accelerate what’s possible. This led us to develop the first technology platform for responsible data use in 2016. Today, with AI representing the latest and most impactful expansion of data yet, OneTrust is once again redefining what responsible innovation looks like. OneTrust, the AI‑Ready Governance Platform™, unifies regulatory intelligence, automation, and connected governance workflows so businesses can continue to move at the speed of AI while ensuring good governance to prevent data misuse at scale. Trusted by thousands of organizations worldwide, OneTrust is shaping the future where trusted data becomes a transformative force for business and society. The Challenge OneTrust is seeking a Staff Software Engineer to join the Reporting and Data Platform team. This is a hands-on individual contributor role focused on designing, building, operating, and improving distributed backend services and data-processing platforms. You will work across Java microservices and Python/PySpark data pipelines, with a strong focus on reliability, scalability, performance, and observability. You will take complex or ambiguous problems from investigation through production delivery and help improve the systems that power reporting and data-driven experiences. Your Mission Technical Ownership and Delivery • Own complex features and technical improvements from discovery through production rollout, making substantial hands-on contributions across backend services and data-processing pipelines. • Investigate ambiguous problems, identify root causes, evaluate trade-offs, and implement pragmatic solutions that improve code quality, maintainability, automated testing, and operational readiness. • Review code and technical designs, document important implementation decisions and system behavior, and partner with product managers, engineers, and other teams to clarify requirements and deliver outcomes. • Apply AI-assisted engineering tools such as Devin, Claude, or similar systems to accelerate delivery while maintaining production-quality design, code, tests, security, and operational readiness. Backend and Distributed Systems • Design and implement production services using Java, Spring Boot, and Maven, including APIs, asynchronous workflows, report generation, aggregation, export, and scheduling capabilities. • Develop event-driven functionality using Kafka and related messaging patterns, and work with caching technologies, relational storage, and service-to-service integrations. • Improve service performance, scalability, fault tolerance, and resource efficiency through appropriate patterns for retries, idempotency, caching, backpressure, concurrency, and failure recovery. • Diagnose issues across services, queues, databases, and downstream dependencies, and modernize established capabilities incrementally while maintaining production stability. Data Engineering • Build and maintain ingestion and transformation pipelines using Python, PySpark, Azure Databricks, and Delta Lake across batch and streaming workloads. • Implement schema evolution, checkpoint management, deduplication, replay, late-arriving-data handling, and standardized data-layer patterns. • Optimize Spark joins, partitioning, Delta operations, cluster utilization, and query performance while troubleshooting failed, delayed, or inefficient Databricks workloads. • Protect tenant boundaries across joins, aggregations, deduplication, and Delta operations; implement data-quality controls; and monitor data freshness, completeness, and correctness. • Work securely with Azure storage, identitie