Database DevOps Engineer

Salesforce•San Francisco, CA

23h•Hybrid

About The Position

Salesforce is looking for a seasoned DevOps Database Engineer who thrives at the intersection of database engineering, modern cloud infrastructure, and applied AI. This role involves owning the reliability, performance, and scalability of database systems while leveraging AI/ML tools to drive automation, intelligent monitoring, and DevOps best practices across a large-scale, mission-critical environment. The CRM Database Sustaining Engineering Team deploys and manages some of the world's most trusted databases, and customers rely on this team to keep their data safe and highly available.

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 such as Hadoop, HDFS, and 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.
Proven ability to support 24x7x365 mission-critical database infrastructure, including Production Incident Management and root cause analysis.
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 such as Grafana, Splunk, or AIOps platforms for anomaly detection and predictive alerting.
Hands-on experience using AI/ML tools for automated query optimization, capacity planning, or intelligent incident response.
Exposure to data engineering for AI pipelines including feature stores, embedding generation, and ETL workflows for model training datasets.
Agile/Scrum methodology and full SDLC exposure, from requirements gathering through operational support.
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, including Production Incident Management and root cause analysis.