Principal Engineer - Data Engineering (AI)

Location: Singapore, Singapore

Company

hirify.global builds storage infrastructure for AI-driven data systems, including hyperscale data centers, cloud platforms, and enterprise infrastructure.

What you will do

• Build and maintain versioned feature engineering pipelines that transform engineering, sensor, and operational data into ML-ready feature sets.
• Design data quality checks for completeness, schema consistency, statistical stability, and label accuracy, and alert ML teams before training is affected.
• Implement training data versioning, lineage tracking, reproducibility controls, and deployment-data drift detection.
• Maintain data contracts, access controls, retention practices, and governance for AI data assets.
• Contribute to real-time sensor ingestion and synthetic data pipeline infrastructure, progressively taking operational ownership over 6–12 months.
• Build the MLOps data layer, including the training dataset registry, feature store, and model input validation integrated with AWS Kubernetes, PortKey, Agent Gateway, LangFuse, AWS Guardrails, and Elastic Search.

Requirements

• Bachelor's or Master's degree in AI, Computer Science, Data Engineering, Electrical Engineering, Applied Mathematics, or a related field; an AI major is preferred, with strong data engineering fundamentals required.
• At least 2–3 years of demonstrated project experience building end-to-end data pipelines in academic, personal, or internship contexts; project evidence is the primary evaluation criterion.
• Strong Python proficiency for pipeline development and strong SQL proficiency, including complex queries, window functions, and data transformation logic.
• Knowledge of scalable batch pipeline architecture, reliability, schema management, fault tolerance, and orchestration with Airflow, Prefect, or AWS Glue Jobs.
• Knowledge of data quality principles, including completeness checks, schema validation, and distribution stability monitoring.
• Knowledge of data versioning and lineage for training-data reproducibility and tracing data origins and transformations.
• Basic understanding of the ML data lifecycle, including how data quality and pipeline design affect model performance, train/test leakage risks, and label-quality impact; prior ML work experience is not required.

Nice to have

• Experience with feature stores such as Feast or Tecton; synthetic data generation with VAE, GAN, or diffusion models; dbt; or DLT.
• Annotation platform integration with Label Studio or CVAT, MES/LIMS integration, or active learning data-loop design.
• Data lakehouse technologies including Iceberg, Dremio, AWS Glue, AWS Lake Formation, or Redshift.

Culture & Benefits

• Full-time role within a global company focused on storage infrastructure and AI-driven data systems.
• WD is committed to diversity, inclusion, belonging, respect, and contribution.
• Accommodation support is available for applicants with disabilities during the application and hiring process.

Hiring process

• Applicants requiring accommodation should contact jobs.accommodations@wdc.com with the requested accommodation, job title, and requisition number.
• WD and its subsidiaries do not request payment for applications or offers; suspected payment requests should be reported through the WD Ethics Helpline or compliance@wdc.com.