Database DevOps Engineer

Role overview

Join Salesforce’s CRM Database Sustaining Engineering Team as a Database DevOps Engineer. You’ll be responsible for the reliability, performance, and scalability of large-scale database systems in a mission-critical, cloud environment, using AI/ML-driven automation and intelligent monitoring.

Key missions / responsibilities

• Ensure database system reliability, performance, and scalability.
• Use AI/ML tools to automate workflows, monitoring, and operational processes.
• Deploy and manage databases in modern cloud environments.
• Implement CI/CD, containerization, and Infrastructure as Code (IaC).
• Collaborate with cross-functional teams to integrate machine learning models into database and data pipeline workflows.
• Provide 24x7x365 production incident management and perform root cause analysis.

Requirements

• Strong understanding of NoSQL, distributed systems, and big data ecosystems (e.g., Hadoop, HDFS, Apache Zookeeper).
• Experience with vector databases (e.g., pgvector or similar) and LLM/AI workloads at scale.
• 10+ years hands-on experience as a Database Engineer/DBA (or equivalent), plus a Bachelor’s degree in CS/Engineering or equivalent practical experience.
• Hands-on experience with CI/CD tools such as Jenkins, Spinnaker, Helm, and Git.
• Hands-on experience with containerization (Docker, Kubernetes/EKS/GKE).
• Infrastructure as Code experience with Terraform.
• Advanced proficiency in at least one scripting language: Python, Ruby, Java, Perl, or C++.
• Deep expertise in Oracle and PostgreSQL performance tuning and schema design.
• Familiarity with AI/ML frameworks and integrating ML models into database/data pipeline workflows.
• Strong communication skills and a proactive, detail-oriented mindset.
• Cloud deployment experience on AWS (e.g., EKS, RDS or equivalent managed services).
• Familiarity with Apache BookKeeper.
• Experience with AI-powered observability/monitoring tools such as Grafana, Splunk, or AIOps platforms.
• Hands-on experience using AI/ML tools for automated query optimization, capacity planning, and/or intelligent incident response.
• Exposure to data engineering for AI pipelines (e.g., feature stores, embedding generation, ETL workflows).
• Experience working in Agile/Scrum with full SDLC exposure.

Nice-to-haves (implicit/related)

• Demonstrated ability to drive automation initiatives from concept to production and deliver innovation.