ML and Data Engineer — Data Quality & PII Compliance

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About the Role

We are looking for an ML/Data Engineer to improve the quality, reliability, and compliance of enterprise data pipelines. You will focus on data quality validation and testing PII/PHI de-identification systems to ensure sanitized data is accurate, consistent, and free from sensitive information leaks. This role combines data engineering, ML evaluation, test automation, and compliance-focused quality assurance.

What You'll Do

• Design and automate validation suites for data pipelines, including schema checks, completeness validation, drift detection, and reconciliation across pipeline stages.
• Perform deep data quality analysis across enterprise data sources and connectors, including topic coherence, domain coverage, consistency, and depth.
• Build adversarial test datasets for PII/PHI de-identification systems, covering edge cases, obfuscated identifiers, multilingual entities, OCR noise, and unusual document formats.
• Evaluate NER and ML-based de-identification systems using precision, recall, F1 score, leak rates, and false-negative analysis.
• Identify and investigate PII leakage risks across raw, processed, and sanitized data.
• Implement automated regression gates in CI/CD to prevent pipeline changes from being deployed without passing data quality and privacy checks.
• Conduct sampling-based human-in-the-loop audits and maintain detailed audit trails for compliance evidence.
• Partner with data and engineering teams to perform root-cause analysis and resolve data inconsistencies, quality issues, and privacy leaks.

You're a Good Fit If You

• Required skills: Machine Learning, Data Engineering
• 5+ years of experience in data engineering, ML engineering, data quality, or a related field.
• Strong Python skills for test automation, data validation, and ML evaluation.
• Experience with testing frameworks and data-quality tools such as pytest, Great Expectations, Pandera, or similar.
• Strong SQL skills and experience validating data across multiple pipeline stages.
• Understanding of PII and PHI categories and de-identification concepts.
• Familiarity with privacy and compliance frameworks such as HIPAA Safe Harbor, GDPR, or LGPD.
• Experience evaluating NER or other ML-based systems using labeled datasets and precision/recall metrics.
• Ability to design reliable evaluations for non-deterministic ML or LLM-based systems.

Role Highlights

Pay & Payout

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