Data Engineer
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Summary
A Singapore-based Data Engineer role centered on privacy-compliant data handling: inspecting, classifying, masking, and anonymizing sensitive regional datasets before ingestion, then maintaining clean, auditable datasets for analytics using SQL, Python, Spark, Airflow, Kafka, and Hive under frameworks like PDPA and GDPR.
Job Description
What You Will Do
Perform data inspection, classification, and profiling on regional datasets to assess sensitivity and compliance requirements. Design and implement data desensitization, masking, anonymization, and pseudonymization pipelines prior to ingestion and exposure. Build and maintain clean, compliant, and well-documented datasets for downstream analytics and reporting. Support user-level and aggregated (fine-to-coarse) data analysis in compliance with regional data regulations. Collaborate with data governance, security, and legal/compliance teams to translate regulatory requirements into technical controls and data workflows. Enforce compliance-by-design principles: no desensitization → no ingestion; no inspection → no exposure. Contribute to continuous improvement of data quality, data lineage, metadata management, and auditability. Participate in platform construction and administration under strict controls (e.g., RBAC, MFA, IP allowlists, separation of duties).
Skills, Qualifications, And Experience We Look For
3–5 years of experience as a Data Engineer or in a closely related role. Strong hands-on experience with data pipelines, ETL/ELT, and data warehousing (e.g., Spark, SQL, Airflow, Kafka, Hive, or equivalent). Solid understanding of data cleaning, validation, and quality assurance techniques. Practical experience handling sensitive or regulated data (PII, user-level data, financial, or operational data). Working knowledge of data compliance concepts and regional regulations, such as SOC2, HIPPA, PDPA, GDPR, EO14117, or similar frameworks. Proficiency in SQL and at least one programming language (Python preferred).
Preferred
Experience with data masking, anonymization, or privacy-preserving analytics. Familiarity with data governance frameworks, metadata management, and audit logging. Experience operating in multi-region or cross-border data environments. Exposure to cloud-based data platforms (AWS, GCP, Azure) and security best practices. Prior collaboration with legal, compliance, or security teams.
What You Will Do
Perform data inspection, classification, and profiling on regional datasets to assess sensitivity and compliance requirements. Design and implement data desensitization, masking, anonymization, and pseudonymization pipelines prior to ingestion and exposure. Build and maintain clean, compliant, and well-documented datasets for downstream analytics and reporting. Support user-level and aggregated (fine-to-coarse) data analysis in compliance with regional data regulations. Collaborate with data governance, security, and legal/compliance teams to translate regulatory requirements into technical controls and data workflows. Enforce compliance-by-design principles: no desensitization → no ingestion; no inspection → no exposure. Contribute to continuous improvement of data quality, data lineage, metadata management, and auditability. Participate in platform construction and administration under strict controls (e.g., RBAC, MFA, IP allowlists, separation of duties).
Skills, Qualifications, And Experience We Look For
3–5 years of experience as a Data Engineer or in a closely related role. Strong hands-on experience with data pipelines, ETL/ELT, and data warehousing (e.g., Spark, SQL, Airflow, Kafka, Hive, or equivalent). Solid understanding of data cleaning, validation, and quality assurance techniques. Practical experience handling sensitive or regulated data (PII, user-level data, financial, or operational data). Working knowledge of data compliance concepts and regional regulations, such as SOC2, HIPPA, PDPA, GDPR, EO14117, or similar frameworks. Proficiency in SQL and at least one programming language (Python preferred).
Preferred
Experience with data masking, anonymization, or privacy-preserving analytics. Familiarity with data governance frameworks, metadata management, and audit logging. Experience operating in multi-region or cross-border data environments. Exposure to cloud-based data platforms (AWS, GCP, Azure) and security best practices. Prior collaboration with legal, compliance, or security teams.