Staff Software Engineer - Reporting, Data Platform & Observability
Primary stack
Job description
About the Position
OneTrust’s mission is to enable innovation through the responsible use of data and AI. We are seeking a Staff Software Engineer to join the Reporting and Data Platform team. This hands-on individual contributor role focuses on designing, building, operating, and improving distributed backend services and data-processing platforms.
Responsibilities
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, identities, secrets, and encryption mechanisms.
Observability, On-Call, and Operational Excellence
- Improve observability across backend services, event-driven workflows, and data pipelines using meaningful metrics, structured logs, traces, and business telemetry.
- Build and maintain actionable dashboards, monitors, and alerts using Datadog and Grafana, applying OpenTelemetry, Prometheus, and Micrometer patterns where appropriate.
- Participate in the on-call rotation and incident-response workflows, using PagerDuty, Datadog monitors, or equivalent platforms to diagnose production issues and drive sustainable resolution.
- Reduce recurring alerts and operational toil by improving alert quality, eliminating noisy or non-actionable monitors, creating runbooks and diagnostic tools, and implementing corrective actions from blameless incident reviews.
- Improve end-to-end correlation and monitor availability, error rates, latency, ingestion lag, data freshness, event throughput, consumer lag, job health, rejected records, checkpoint health, tenant-specific failures, data-quality violations, and Spark resource utilization.
Requirements
Required
- 8+ years of experience building and operating production software systems as a highly autonomous individual contributor.
- Strong proficiency in Java and Spring Boot, with experience designing and operating distributed systems and microservices.
- Production experience with asynchronous or event-driven systems, preferably Apache Kafka.
- Strong experience with Python, PySpark, Apache Spark, and Delta Lake, plus production experience with Azure Databricks or a comparable managed Spark platform.
- Hands-on experience with test-driven development, automated testing strategies, and quality gates that support fast, reliable delivery.
- Strong understanding of metrics, logs, distributed tracing, dashboards, monitoring, and alerting, including hands-on experience with Datadog and Grafana.
- Experience creating or responding to PagerDuty incidents, Datadog alerts, or equivalent production alerting workflows, and willingness to participate in an on-call rotation.
- Experience using AI engineering tools such as Devin, Claude, or similar systems to produce production-ready code, tests, documentation, and operational improvements.
- Strong design-thinking skills and the ability to reduce delivery-cycle time through clear architecture, smaller increments, reusable patterns, and pragmatic technical trade-offs.
- Ability to independently diagnose complex performance and reliability problems and communicate implementation decisions and technical trade-offs clearly.
Preferred
- Experience with both batch and streaming data pipelines and with optimizing Spark or Databricks workloads for performance, reliability, and cost.
- Experience with Databricks SQL, Databricks SDKs, Delta operations, and schema migrations.
- Familiarity with Azure Blob Storage, Azure Identity, and Azure Key Vault.
- Experience operating reporting, analytics, dashboard, or large-scale export systems.
- Experience with Kubernetes, containers, CI/CD, and infrastructure as code.
- Experience defining or applying service-level indicators, service-level objectives, and error budgets, and using incident and alert trends to prioritize engineering work.
- Understanding of data governance, encryption, audit-ability, and tenant isolation.
- Experience modernizing established production systems incrementally.
Salary Range
$139,725 - $209,588 USD
Where we Work
We are embracing an office-first culture, encouraging three days a week in office for most roles, with meaningful opportunities to collaborate and celebrate in person.
Benefits
As an employee at OneTrust, you will be part of the OneTeam. That means you’ll receive support physically, mentally, and emotionally so that you can do your best work both in and out of the office. This includes comprehensive healthcare coverage, flexible PTO, equity RSUs, annual performance bonus opportunities, retirement account support, 14+ weeks of paid parental leave, career development opportunities, company-paid privacy certification exam fees, and much more. Specific benefits differ by country. For more information, talk to your recruiter or visit onetrust.com/careers.
About the Company
OneTrust is the AI-Ready Governance Platform™, unifying 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.
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At DocuVision, we believe that a person’s digital privacy is a human right. The status quo of data harvesting and microtargeting has had a toxic effect, and we are at the brink of a new era. DocuVision helps companies find all the personal data they have collected and helps them purge it, while still retaining business value. We seek to achieve our vision by training the best NLP algorithms which continue to be tuned through machine learning without storing any private data. Our startup is an early-stage UC Berkeley Skydeck company and we are located in Silicon Valley , California, with a globally distributed team. We’re seeking like-minded people who are passionate about data privacy.