Data Engineer

• At least five years’ experience in data engineering, software engineering, security engineering or a related discipline. \n

• At least two years implementing practical data protection capabilities, such as discovery, classification, masking, tokenization or access controls. \n

• Strong Python and SQL skills, with evidence of building automation and platform integrations. \n

• Hands-on experience with cloud data environments such as AWS, GCP or Databricks. \n

• An understanding of sensitive data exposure, DSPM principles and how to remediate protection gaps. \n

• Experience with APIs, CI/CD, automated testing and supporting production systems. \n

• The ability to explain technical findings clearly and work across engineering and business teams. \n, Experience with enterprise data security or governance platforms, metadata management, data lineage or regulated environments would be useful.

We’re recruiting a hands-on Data Engineer to strengthen data protection across a complex cloud environment. This role combines Python and SQL engineering with practical security challenges: finding sensitive information, controlling how it is exposed, and automating protection as data moves between platforms.

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You’ll work closely with engineering, cybersecurity and governance teams, turning requirements into working software. Your impact will be visible through better protection coverage, faster remediation and fewer manual processes.

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What you’ll do

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• Build automated workflows to discover and classify sensitive information across structured and unstructured data. \n

• Implement masking, tokenization and other controls that protect data while supporting its use. \n

• Develop Data Security Posture Management (DSPM) capabilities to identify exposure, misconfigurations and gaps in protection. \n

• Write Python and SQL integrations, APIs and custom connectors across cloud data platforms. \n

• Create monitoring and dashboards that show where controls are working and where action is needed. \n

• Take solutions through development, testing, deployment and ongoing operation, using sound software engineering practices. \n

• Help engineering teams and data owners adopt the tools you build. \n

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