Database DevOps Engineer

Salesforce•San Francisco, CA

1d•Hybrid

About The Position

Salesforce is seeking a seasoned DevOps Database Engineer to join the CRM Database Sustaining Engineering Team. This role is crucial for deploying and managing some of the world's most trusted databases, ensuring customer data is safe and highly available. The ideal candidate will excel at the intersection of database engineering, modern cloud infrastructure, and applied AI. You will be responsible for the reliability, performance, and scalability of our database systems, utilizing AI/ML tools to automate processes, enhance monitoring, and implement DevOps best practices in a large-scale, mission-critical environment.

Requirements

Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
10+ years of hands-on experience as a Database Engineer, DBA, or similar role.
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.
Hands-on experience with CI/CD tooling (Jenkins, Spinnaker, Helm, GIT).
Hands-on experience with containerization (Docker, Kubernetes/EKS/GKE).
Hands-on experience with Infrastructure as Code (Terraform).
Solid understanding of NoSQL, distributed systems, and big data ecosystems (Hadoop, HDFS, Apache Zookeeper).
Familiarity with AI/ML frameworks and experience integrating machine learning models with database and data pipeline workflows.
Experience working with vector databases (pgvector or equivalent) and supporting AI-driven applications and LLM-based workloads at scale.
Strong communicator with a proactive, detail-oriented mindset.
Ability to thrive in a fast-paced, large-scale cloud environment.

Nice To Haves

Cloud deployment experience on AWS (EKS, RDS, or equivalent managed services).
Familiarity with Apache Bookkeeper.
Experience with AI-powered observability and monitoring tools (Grafana, Splunk, or AIOps platforms).
Hands-on experience using AI/ML tools for automated query optimization, capacity planning, or intelligent incident response.
Exposure to data engineering for AI pipelines (feature stores, embedding generation, ETL workflows for model training datasets).
Agile/Scrum methodology and full SDLC exposure.
Demonstrated ability to drive automation initiatives and deliver innovation from concept to production.

Responsibilities

Own the reliability, performance, and scalability of database systems.
Leverage AI/ML tools to drive automation, intelligent monitoring, and DevOps best practices.
Support 24x7x365 mission-critical database infrastructure.
Perform Production Incident Management and root cause analysis.
Drive automation initiatives and deliver innovation from concept to production.