Data Engineer - Compliance
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Summary
Data Engineer building the compliance 'trust layer' of a fast-growing AI company's data infrastructure in Singapore: designing data masking/anonymization pipelines, profiling and classifying sensitive datasets, and translating regulations (GDPR, PDPA, SOC2) into technical controls using SQL, Python, Spark, Airflow, Kafka, and Hive.
Our client is a fast-growing AI company with a market-leading product. As they expand globally, they are seeking a specialized Data Engineer to build the 'trust layer' of their data infrastructure. This is a hands-on role where you will enforce 'Compliance-by-Design' principles, ensuring that speed and innovation never compromise data security or regulatory adherence.
What You Will Do Architect Compliance Pipelines:
Design and implement robust pipelines for data desensitization, masking, anonymization, and pseudonymization. You will ensure that raw data is transformed into safe assets
before
ingestion or exposure. Data Inspection & Classification:
Lead the profiling of regional datasets to assess sensitivity levels. You will act as the gatekeeper, enforcing strict rules:
no desensitization
no ingestion; no inspection
no exposure. Enable Safe Analytics:
Build and maintain clean, documented datasets that support downstream analytics. You will facilitate both user-level and aggregated (fine-to-coarse) analysis while strictly adhering to regional data boundaries. Governance Implementation:
Collaborate with Security, Legal, and Governance teams to translate complex regulatory requirements into technical controls (RBAC, MFA, IP allowlists) and automated workflows. Platform & Quality Assurance:
Manage platform administration under strict security controls while driving continuous improvement in data quality, lineage, metadata management, and auditability.
Skills & Qualifications Experience:
3–5 years of experience in Data Engineering, ETL/ELT design, and data warehousing. Technical Stack:
Strong proficiency in
SQL
and
Python . Hands-on experience with tools such as Spark, Airflow, Kafka, Hive, or equivalent. Privacy & Compliance:
Practical experience handling sensitive data (PII, financial, user-level). You must have working knowledge of frameworks such as
GDPR, PDPA, SOC2, HIPAA, or EO14117. Data Quality:
A solid understanding of data cleaning, validation, and profiling techniques.
Preferred Qualifications Cross-Border Expertise:
Experience operating in multi-region data environments, specifically dealing with cross-border data transfer regulations. Advanced Privacy Tech:
Familiarity with privacy-preserving analytics, advanced data masking, and anonymization techniques. Cloud & Security:
Exposure to cloud-native platforms (AWS, GCP, Azure) and a strong grasp of security best practices. Cross-Functional Ops:
Prior experience acting as the technical bridge between engineering and legal/compliance teams.
What You Will Do Architect Compliance Pipelines:
Design and implement robust pipelines for data desensitization, masking, anonymization, and pseudonymization. You will ensure that raw data is transformed into safe assets
before
ingestion or exposure. Data Inspection & Classification:
Lead the profiling of regional datasets to assess sensitivity levels. You will act as the gatekeeper, enforcing strict rules:
no desensitization
no ingestion; no inspection
no exposure. Enable Safe Analytics:
Build and maintain clean, documented datasets that support downstream analytics. You will facilitate both user-level and aggregated (fine-to-coarse) analysis while strictly adhering to regional data boundaries. Governance Implementation:
Collaborate with Security, Legal, and Governance teams to translate complex regulatory requirements into technical controls (RBAC, MFA, IP allowlists) and automated workflows. Platform & Quality Assurance:
Manage platform administration under strict security controls while driving continuous improvement in data quality, lineage, metadata management, and auditability.
Skills & Qualifications Experience:
3–5 years of experience in Data Engineering, ETL/ELT design, and data warehousing. Technical Stack:
Strong proficiency in
SQL
and
Python . Hands-on experience with tools such as Spark, Airflow, Kafka, Hive, or equivalent. Privacy & Compliance:
Practical experience handling sensitive data (PII, financial, user-level). You must have working knowledge of frameworks such as
GDPR, PDPA, SOC2, HIPAA, or EO14117. Data Quality:
A solid understanding of data cleaning, validation, and profiling techniques.
Preferred Qualifications Cross-Border Expertise:
Experience operating in multi-region data environments, specifically dealing with cross-border data transfer regulations. Advanced Privacy Tech:
Familiarity with privacy-preserving analytics, advanced data masking, and anonymization techniques. Cloud & Security:
Exposure to cloud-native platforms (AWS, GCP, Azure) and a strong grasp of security best practices. Cross-Functional Ops:
Prior experience acting as the technical bridge between engineering and legal/compliance teams.