Description
- We're Salesforce, the Customer Company, inspiring the future of business with AI + Data + CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you've come to the right place.
The Data Security Fabric team is seeking a Lead/Senior Software Engineer to help architect and build a secure, cloud-native, and highly scalable Data Platform designed to measure, mitigate, and reduce enterprise risk. By unifying disparate security data across the organization, our platform will provide actionable insights to proactively identify risks and automate remediation efforts.
As a Lead/Senior Engineer on our team, you will be responsible for designing and implementing a robust data platform that collects and processes critical security signals from a wide range of sources. These include comprehensive asset information (both hardware and software) across Salesforce, vulnerability data from large-scale scanning tools such as Tenable and Prisma, user identity and access data, security signals from 10+ Salesforce platforms (e.g., Core, Hyperforce,
Data Cloud, Slack, Tableau, Heroku, MuleSoft), and other security signals from over 15 different environments like AWS, GCP, CRM systems, and third-party vendor platforms.
Responsibilities
- Learn and adapt to Salesforce security strategies, security goals, security objectives and security capabilities to improve security posture.
- Lead the design and architecture of a highly scalable and secure data platform that ingests and processes diverse security data sources across 10+ Salesforce platforms and 15+ external environments.
- Build and optimize data pipelines to collect, store, and analyze security signals from tools and platforms (e.g. vulnerability scanners, asset management systems, identity and access control systems) running on multiple environments (AWS,
GCP, Salesforce, CRM, vendor systems, etc.).
- Design and integrate AI/ML and LLM-driven capabilities into the platform — including RAG over security data, agentic triage/remediation workflows, anomaly detection, and risk scoring — with strong attention to evals, guardrails, and
cost/latency tradeoffs.
- Work closely with cross-functional teams (e.g., Engineering, Security Operations, Risk Management, Product Security and Data Science) to align the data platform with business and security goals.
- Operate in an Agile development environment, including participating in daily scrums.
- Support the team's engineering excellence by performing code reviews and mentoring senior team members.
- Provide technical leadership to a team of engineers, driving best practices in software development, security, AI-assisted development, and cloud-native architecture. Mentor junior and mid-level engineers, fostering a culture of continuous
learning, innovation, and excellence.
- Champion effective use of AI-assisted development tools (e.g., Claude Code, Cursor, Copilot) across the team — establishing patterns for agent-driven workflows, code review, and productivity while maintaining code quality and security.
- Own and deliver initiatives adding new features to meet ever growing product demands.
- Adapt to change quickly and eagerly: changing requirements, changing priorities, changing strategies.
- Advocate security and secure practices throughout Salesforce, including secure AI/agent design (prompt injection defenses, least-privilege tool access, data handling for LLM contexts).
Required Skills/Experience
- Distributed systems and data engineering: Expertise in designing, implementing and operating high-scale distributed systems architectures and concepts, including the following:
- High-performance, high-availability (99.99%), and highly fault-tolerant systems
- Large scale infrastructure systems
- Docker-based development, especially experience using EKS
- Configuration management systems, including Infrastructure-as-Code (IAC), Terraform, Puppet
- Data Tech: Apache Spark & Kafka, Hadoop, SQL/NoSQL
- Programming: Proficiency in object-oriented and multi-threaded programming in at least one of the following languages: Python, Golang, Java/Scala
- Software design: Demonstrated expertise in applying systems patterns (e.g., Client-server, N-tier, Primary/secondary, MVC) and API constructions (e.g., Swagger, OpenAPI)
- Operating systems: Development and software management on Linux (e.g., CentOS or RHEL)
- Security: Strong fundamentals knowledge in security concepts: authentication/authorization frameworks (e.g., SSO, SAML, OAuth, etc), secure transport (e.g., TLS), identity management (e.g., certificates, PKI)
- Applied AI/ML: Hands-on experience integrating LLMs or ML models into production systems — including at least two of: RAG pipelines, agent/tool-use frameworks (e.g., MCP, LangGraph), prompt engineering, evals/observability for LLM apps,
fine-tuning, or embeddings/vector stores.
- AI-assisted engineering: Demonstrated fluency with agentic coding tools (Claude Code, Cursor, Copilot, or equivalent) as a daily driver — able to guide the team on effective and safe usage patterns.
- Communication: Excellent oral and written communication skills
- Ability to manage multiple projects simultaneously, meet deadlines, and adapt to shifting priorities
- Team: Ability to value team success beyond personal contributions
- Vision Execution: Ability to translate strategic or operational goals to technical and tactical requirements and architecture design.
Preferred / Nice-to-have
- Experience applying AI/ML to security use cases — e.g., anomaly detection, alert triage, vulnerability prioritization, or SOC automation.
- Experience with MCP (Model Context Protocol), tool-use frameworks, or building agentic workflows on top of security data.
- Familiarity with LLM cost/latency optimization, guardrails, prompt-injection defense, and evaluation frameworks.
- Experience with vector databases and embedding-based retrieval over structured/unstructured security data.