Jobs at Sonatype

Design, develop, test, and maintain backend services supporting provisioning, licensing, and purchasing. Improve cloud infrastructure, automation, reliability, observability, scalability, and service delivery. Collaborate on architecture and cross-functional delivery, troubleshoot production issues, participate in code reviews, and mentor engineers while remaining hands-on in software development.

Provides technical leadership for applied AI and data science initiatives from experimentation through production. Builds machine learning and generative AI solutions for anomaly detection, malicious behavior detection, fraud analysis, and security use cases. Designs LLM, retrieval, embedding, and agentic workflows; establishes evaluation and monitoring practices; partners with engineering and MLOps on reliable deployment; advises stakeholders; mentors technical contributors; and promotes secure, scalable, and responsible AI practices.

Analyze product usage, feature adoption, telemetry, and financial data to generate actionable insights. Own data modeling, dashboards, KPI development, and end-to-end analytics delivery while partnering with stakeholders on product initiatives. Mentor associate-level analysts and maintain strong execution metrics for analytics work. The role requires advanced SQL and Python, Databricks and PySpark expertise, visualization experience, and SaaS product analytics experience.

Develop and maintain scalable backend services supporting product provisioning, licensing, and purchasing. Design architectures, write tested Java-based code, troubleshoot production issues, improve reliability, and contribute to cloud-native infrastructure. Collaborate with product and engineering teams, participate in technical decisions and code reviews, mentor engineers, and provide hands-on technical leadership across distributed systems and microservices.

Lead the Data Identity team's technical direction: own identity data model, architect streaming and batch pipelines, design stable data contracts, lead vendor SBOM ingestion, apply AI/ML where appropriate, improve reliability, cost, and incident response, and collaborate with downstream teams and external vendors.

Design, build, and maintain scalable data pipelines, optimize Databricks/Spark and Delta Lake architectures, deliver trusted datasets for analytics and ML, implement observability and data quality, and drive platform architecture and team mentorship.

Lead applied AI projects from prototype to production, building and validating ML and GenAI models (LLMs, embeddings, RAG, agentic workflows) for malicious-behavior, anomaly, and fraud detection. Advise product and engineering teams, design experiments and evaluation pipelines, translate research into scalable APIs/workflows, and ensure data governance, privacy, and ethical practices.