Staff Software Engineer - Reporting, Data Platform & Observability

OneTrust•Atlanta, GA

5d•$139,725 - $209,588•Hybrid

About The Position

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.

Requirements

Strong professional 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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.