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Job Category
Software Engineering
Job Details
The Data Security Fabric team is seeking a Lead Data 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 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 (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.
You will use leading-edge technologies to build the next-generation security data platform, creating a vendor-agnostic core security system. AI and machine learning are first-class capabilities of this platform - you will help design LLM- and ML-driven workflows for security signal enrichment, anomaly detection, risk scoring, and automated triage, and integrate agentic patterns (RAG, tool-use, evals) into the platform's remediation and investigation flows. You will also help establish data governance and quality frameworks that support risk management, regulatory compliance, and continuous security improvement across the enterprise.
This is an exciting opportunity for experienced engineers with a passion for distributed systems, big data processing, AI/ML, and security. You will play a key role in shaping the future of security at Salesforce, helping ensure the platform's scalability, reliability, and alignment with industry best practices. Your work will have a direct impact on Salesforce's security strategy - driving innovation, improving risk management, using AI to accelerate detection and response, and enabling automation of security remediation across the organization.
If you're an engineer looking to grow your technical expertise in cloud-native systems, cybersecurity, big data processing, and applied AI, this role offers strong opportunities for professional development, along with meaningful visibility and business impact.
Responsibilities- Learn and adapt to Salesforce's security strategies, goals, objectives, and capabilities to help 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 across 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.
- Participate in an Agile development environment, including daily syncs.
- Support the team's engineering excellence through code reviews and by 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 engineers at all levels, 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 that add new features to meet growing product demands.
- Adapt quickly to changing requirements, priorities, and strategies.
- Advocate for 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 - Industry experience: 10+ years for LMTS, including 5+ years in SaaS, PaaS, or IaaS software development.
- Education: M.Sc./B.E. in Computer Science Engineering (or equivalent experience).
- Distributed systems and data engineering: Expertise designing, implementing, and operating high-scale distributed systems, including:
- High-performance, high-availability (99.99%), and highly fault-tolerant systems
- Large-scale infrastructure systems
- Docker-based development, particularly with EKS
- Configuration management systems, including Infrastructure-as-Code (IaC), Terraform, Puppet
- Data technology: Apache Spark & Kafka, Hadoop, SQL/NoSQL.
- Programming: Proficiency in object-oriented and multi-threaded programming in at least one of: Python, Golang, Java/Scala.
- Software design: Demonstrated expertise applying system patterns (e.g., client-server, N-tier, primary/secondary, MVC) and API construction (e.g., Swagger, OpenAPI).
- Operating systems: Experience developing and managing software on Linux (e.g., CentOS or RHEL).
- Security: Strong foundational knowledge of security concepts - authentication/authorization frameworks (e.g., SSO, SAML, OAuth), secure transport (e.g., TLS), and 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, with the ability to guide the team on effective and safe usage patterns.
- Communication: Strong oral and written communication skills.
- Able to manage multiple projects at once, meet deadlines, and adapt to shifting priorities.
- Team orientation: Values team success alongside personal contributions.
- Vision execution: Able to translate strategic or operational goals into 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.
Unleash Your Potential
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