Senior Security Engineer - Data Security

Sigma Computing•San Francisco, CA

2h•$175,000 - $220,000•Onsite

About The Position

We are looking for a highly technical Senior Security Engineer who is passionate about protecting data across modern SaaS, cloud, endpoint, and analytics environments. This role is ideal for someone who enjoys solving complex data security challenges through engineering, automation, and AI rather than relying solely on commercial tools. You will help build, scale, and continuously evolve Sigma’s data security capabilities across SaaS applications, cloud platforms, endpoints, collaboration tools, AI systems, and data warehouses. You will be joining a dynamic and highly technical Security Engineering team at Sigma. This is a hands-on engineering role for builders, innovators, and problem-solvers who want to create scalable security solutions that protect the organization's most critical asset - data.

Requirements

5+ years of hands-on experience in Security Engineering, Data Security, SaaS Security, or related cybersecurity roles.
Bachelor or Masters in Computer Science, Cyber Security, or similar fields.
Strong software engineering mindset with demonstrated experience building production-grade tools, automation, and security platforms.
Proven ability to build versus buy whenever practical and create scalable internal solutions.
Experience designing and implementing platforms like Insider threat and data protection technologies (SIEM, UBA, DSPM, SSPM,DLP, Browser Protection, Endpoint detection)
Strong understanding of data security principles including data classification, data governance, access controls, encryption, key management, and data lifecycle management.
Hands-on experience securing SaaS platforms, cloud environments, endpoints, collaboration systems, and modern data platforms.
Experience leveraging AI to improve data protection, investigations, classification, and security workflows.
Strong analytical, communication, and problem-solving skills.

Nice To Haves

Experience securing Snowflake, Databricks, BigQuery, Redshift, or similar modern data platforms.
Strong understanding of AI/LLM data protection risks, data leakage prevention, and AI governance.
Building programs and solving complex security challenges across enterprise-wide data Security Programs.
Experience building internal security products, platforms, and developer-focused security tooling.
CISSP, CCSP, or equivalent certifications.

Responsibilities

Design, build, and maintain security platforms, services, APIs, automation, and internal tooling that protect Sigma's data across cloud, SaaS, endpoint, and analytics environments.
Develop scalable solutions for data discovery, classification, governance, access controls, monitoring, and protection using modern engineering practices and automation.
Build and mature capabilities around CASB, DLP, DSPM, SSPM, insider risk, and data access governance to reduce the risk of unauthorized access, sharing, or exposure of sensitive information.
Build and maintain DLP controls and tooling across multiple cloud, OS environments and decentralized datasets, implementing and tuning prevention capabilities that protect against sophisticated data security incidents.
Drive automation across DLP engineering and operations by leveraging LLMs, agentic AI, and an automation-first mindset to streamline workflows, reduce manual effort, and scale data protection coverage.
Design and implement security controls across SaaS applications, collaboration platforms, endpoints, and business-critical services.
Partner with IT, Engineering, Privacy, Legal, and Business teams to securely enable productivity while maintaining strong data protection controls.
Design and implement security controls for modern data platforms, data warehouses, AI-enabled systems, and cloud-native environments.
Assess data architectures, access models, and security configurations while driving secure-by-design principles through architecture reviews, threat modeling, and security best practices.
Continuously identify opportunities to leverage AI, automation, and modern engineering practices to solve data security challenges.
Evaluate emerging technologies and drive adoption where they provide measurable security and operational value.