全职 Principal Data Engineer 工作, 薪水, APJR CONSULTANCY SERVICES PRIVATE LIMITED Selangor 公司招聘中 - Ricebowl

Principal Data Engineer

Working location: PJ area

Role Mission: 

To provide technical leadership Data organization by designing, delivering, and operationalizing complex data pipelines, curated datasets, and reusable engineering patterns on the cloud-native data platform. This role drives technical excellence across data ingestion, transformation, modeling, DataOps, and production reliability to enable trusted, scalable, and self-service analytics across business domains. 

We need:

• 7–10+ years of experience in cloud-native data engineering, with strong hands-on architecture, delivery, and production support experience on AWS & Snowflake. 
• Strong track record delivering complex data engineering initiatives independently, with the ability to operate across both build and run responsibilities. 
• Experience partnering with BI and business teams to design modelled datasets and enable self-service analytics. 
• Demonstrated technical leadership through design reviews, code reviews, mentoring, and troubleshooting guidance without formal team management responsibility. 
• Deep hands-on technical expertise, including: 
• Snowflake: schema design, Streams/Tasks, Stored Procedures, UDFs, RBAC-aware development, performance tuning, cost monitoring, Cortex AI, and Streamlit. 
• Airflow or similar data orchestration tools: DAG design, orchestration, scheduling, dependency management, retry patterns, and observability. 
• Python and SQL: pipeline scripting, transformation logic, data validation, and operational tooling. 
• ELT/ETL frameworks: Airbyte, Fivetran, and custom connector understanding or development. 
• AWS services: S3 (data lake structures and archival), Lambda, KMS, Transfer Family, CloudWatch, and SageMaker. 
• Demonstrated success delivering medallion architecture (Bronze/Silver/Gold) and enabling self-service data use cases. 
• Experience implementing automated data quality controls, remediation workflows, and data lineage-aware engineering practices across enterprise datasets. 
• Familiarity with machine learning or AI integration using platforms like AWS SageMaker. 
• Proven ability to troubleshoot complex data issues, lead root-cause analysis, and improve production stability through mechanisms rather than repeated manual intervention. 
• Track record of raising team engineering quality through reusable patterns, operational discipline, and technical coaching.