Job Description
Data Protection Engineer, 170k plus bonus, Fortune Insurance Company, Midtown Manhattan
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HYBRID MODEL, 3 DAYS ON SITE
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MUST BE A GREEN CARD OR US CITIZEN
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MUST HAVE STRONG TECHNICAL BACKGROUND, PYTHON, SQL, AND DATABRICKS
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The Data Protection Engineer strengthens enterprise data protection capabilities by designing, implementing, automating, and maintaining technical controls that protect sensitive information across cloud-based data environments, in support of our obligations as a regulated financial services and insurance company.
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As a hands-on individual contributor within the Data Governance and Data Risk Management function, this role focuses on sensitive data discovery and classification, Data Security Posture Management (DSPM), data masking and tokenization, protection monitoring, and integration of enterprise data protection technologies using platforms such as Databricks, Securiti.ai, Anomalo, and Atlan.
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The engineer works closely with Data Governance, Data Risk Management, Cybersecurity, Cloud Engineering, and business and technology stakeholders to identify protection gaps, implement scalable solutions, improve visibility into sensitive data exposure, and drive adoption of data protection tools and practices.
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- 5+ years of professional experience in software engineering, data engineering, data security engineering, or a related technical discipline.
- 2+ years of hands-on experience implementing data protection, data security, or DSPM capabilities (for example, sensitive data discovery, classification, masking, tokenization, or access controls).
- Strong programming skills in Python and SQL, including building automation, integrations, and data processing workflows.
- Hands-on experience with cloud-based data environments such as AWS, GCP, or Databricks.
- Practical knowledge of sensitive data discovery, classification, masking, tokenization, and data protection controls for structured and unstructured data.
- Understanding of DSPM principles, sensitive data exposure identification, and technical remediation approaches.
- Experience integrating enterprise platforms through APIs, custom connectors, or automated workflows.
- Working knowledge of data security concepts, including authentication, authorization, secrets management (for example, AWS Secrets Manager), and secure data handling.
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