Database Devops Engineer

Salesforce Salesforce · Enterprise · San Francisco, CA +1

Salesforce is seeking a seasoned DevOps Database Engineer to manage and optimize their large-scale, mission-critical database systems. The role involves leveraging AI/ML tools for automation, intelligent monitoring, and DevOps best practices, with a focus on reliability, performance, and scalability. Experience with Oracle, PostgreSQL, CI/CD, containerization, Infrastructure as Code, NoSQL, distributed systems, and vector databases is required. Familiarity with AI/ML frameworks and supporting AI-driven applications and LLM workloads is also essential.

What you'd actually do

  1. Own the reliability, performance, and scalability of our database systems while leveraging AI/ML tools to drive automation, intelligent monitoring, and DevOps best practices across a large-scale, mission-critical environment.
  2. Advanced proficiency in at least one scripting language (Python, Ruby, Java, Perl, or C++), with deep expertise in Oracle and PostgreSQL performance tuning and schema design.
  3. Hands-on experience with CI/CD tooling (Jenkins, Spinnaker, Helm, GIT), containerization (Docker, Kubernetes/EKS/GKE), and Infrastructure as Code (Terraform).
  4. Solid understanding of NoSQL, distributed systems, and big data ecosystems such as Hadoop, HDFS, and Apache Zookeeper.
  5. Proven ability to support 24x7x365 mission-critical database infrastructure, including Production Incident Management and root cause analysis.

Skills

Required

  • Database Engineering
  • DBA
  • Oracle performance tuning
  • PostgreSQL performance tuning
  • schema design
  • Python
  • Ruby
  • Java
  • Perl
  • C++
  • CI/CD tooling
  • Jenkins
  • Spinnaker
  • Helm
  • GIT
  • Docker
  • Kubernetes
  • EKS
  • GKE
  • Terraform
  • NoSQL
  • distributed systems
  • Hadoop
  • HDFS
  • Apache Zookeeper
  • vector databases
  • pgvector
  • Production Incident Management
  • root cause analysis

Nice to have

  • AWS
  • EKS
  • RDS
  • Apache Bookkeeper
  • AI-powered observability
  • monitoring tools
  • Grafana
  • Splunk
  • AIOps platforms
  • anomaly detection
  • predictive alerting
  • automated query optimization
  • capacity planning
  • intelligent incident response
  • data engineering for AI pipelines
  • feature stores
  • embedding generation
  • ETL workflows
  • model training datasets
  • Agile/Scrum methodology
  • full SDLC

What the JD emphasized

  • 10+ years
  • 24x7x365