Staff Software Engineer - Reporting, Data Platform & Observability

OneTrust•Atlanta, GA

3d•$139,725 - $209,588

About The Position

OneTrust is 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. The engineer will work across Java microservices and Python/PySpark data pipelines, emphasizing reliability, scalability, performance, and observability. The role involves taking complex or ambiguous problems from investigation through production delivery and improving systems that power reporting and data-driven experiences. The position owns complex features and technical improvements from discovery through production rollout, contributing directly across backend services and data pipelines. It designs and implements Java and Spring Boot services, APIs, asynchronous workflows, report generation, aggregation, export, and scheduling capabilities; develops event-driven functionality with Kafka; and improves performance, scalability, fault tolerance, and resource efficiency. It also builds and maintains batch and streaming data pipelines using Python, PySpark, Azure Databricks, and Delta Lake, including data quality, tenant isolation, schema evolution, and workload optimization. The engineer improves observability through metrics, structured logs, traces, dashboards, monitors, and alerts, and participates in on-call and incident response. The role calls for ownership through design, implementation, testing, deployment, production validation, and ongoing operation; using production evidence to address root causes; and balancing delivery speed, reliability, performance, security, cost, and maintainability. OneTrust describes an office-first culture, encouraging three days per week in the office for most roles, with role-specific requirements or flexibility to be confirmed with a recruiter.

Requirements

At least 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 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.
Committed to test-driven development, early validation, and production-quality engineering practices.
Ability to work across service, platform, data, and partner-team boundaries without formal authority.
Pragmatic judgment about whether to build, reuse, or modernize, considering reliability, performance, security, cost, and maintainability.

Nice To Haves

Experience with both batch and streaming data pipelines and 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, auditability, and tenant isolation.
Experience modernizing established production systems incrementally.

Responsibilities

Own complex features and technical improvements from discovery through production rollout, making hands-on contributions across backend services and data-processing pipelines.
Investigate ambiguous problems, identify root causes, evaluate trade-offs, and improve code quality, maintainability, automated testing, and operational readiness.
Review code and technical designs, document implementation decisions and system behavior, and partner with product managers, engineers, and other teams to clarify requirements and deliver outcomes.
Use AI-assisted engineering tools to accelerate delivery while maintaining production-quality design, code, tests, security, and operational readiness.
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 using appropriate retries, idempotency, caching, backpressure, concurrency, and failure-recovery patterns.
Diagnose issues across services, queues, databases, and downstream dependencies, and incrementally modernize established capabilities while maintaining production stability.
Build and maintain batch and streaming ingestion and transformation pipelines using Python, PySpark, Azure Databricks, and Delta Lake.
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; troubleshoot 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.
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, removing 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.
Own work through design, implementation, testing, deployment, production validation, and ongoing operation.
Use production evidence and telemetry to prioritize improvements and resolve root causes, while reducing alert volume and operational toil without hiding genuine system risks.
Collaborate constructively across service, platform, data, and partner-team boundaries without formal authority.