Staff Security Engineer - Data Security

Lead the development, implementation, and ongoing maintenance of comprehensive security strategies and solutions.

Design and deploy advanced security controls to protect the business.

Work across disciplines to shape our security services strategy and execution

Set and uphold the standard for security processes to support high-quality engineering

Mentor and galvanize new engineers to do their best work

BS/BTech (or higher) in Computer Science, Information Technology, Cybersecurity or a related field

8+ years of experience in software or security engineering within Cloud Native environments

Data Security

Cloud & Data Platforms: Experience with modern cloud data platforms (e.g., Postgres, Snowflake, Databricks, AWS S3) and data architectures (data warehouses, data lakes, lakehouses).

Data Security Posture Management (DSPM): Deep experience with data discovery, classification, and other DSPM focused areas to help protect sensitive data across the organization.

Technical & Scripting: Proficiency in Python (or a similar language) and regex patterns for automation and tooling integration.

Compliance & Privacy: Foundational understanding of protecting PII/PHI and healthcare regulatory requirements (e.g., HIPAA).

Experience architecting, developing, and deploying large-scale distributed systems at scale

Experience with cloud technologies, e.g., AWS, Azure, GCP

Experience building continuous integration and continuous development (CI/CD) pipelines

Familiarity with server-side web technologies (eg: Java, Python, Scala, C#, C++, Go)

4+ years of experience acting as a trusted technical decision-maker in a team setting, solving for short-term and long-term business value

Experience with health-tech systems, like Electronic Health Records, Clinical data, etc.

DSPM Tools: Hands-on experience with Data Security Posture Management platforms (e.g., Wiz DSPM, BigID, Cyera, Bedrock Data).

Pipeline Integration: Experience embedding security controls directly into data pipelines and ETL/ELT workflows.

Data Lifecycle: Familiarity with data retention, lifecycle management, and legal hold processes.

Data Protection: Familiarity and experience with Tokenization, Encryption, or cryptography tools or processes that help protect sensitive data at rest.

Industry Context: Prior experience working in healthcare, health tech, or another highly regulated, compliance-heavy environment.