Description
The CRM Database Sustaining Engineering Team deploys and manages some of the world's most trusted databases. Our customers rely on us to keep their data safe and highly available.
About the Role
We're looking for a seasoned DevOps Database Engineer who thrives at the intersection of database engineering, modern cloud infrastructure, and applied AI. You'll 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.
Requirements
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience, with **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++), with deep expertise in **Oracle and PostgreSQL** performance tuning and schema design.
- Hands-on experience with CI/CD tooling **(Jenkins, Spinnaker, Helm, GIT)**, containerization **(Docker, Kubernetes/EKS/GKE)**, and 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 and the ability to thrive in a fast-paced, large-scale cloud environment.
Preferred Qualifications
- Cloud deployment experience on AWS (EKS, RDS, or equivalent managed services)and 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 eature 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.
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.