Senior Principal Data Engineer Lead

Role: Senior Principal Data Engineering Lead
Location: Singapore

To lead and scale the Data Engineering, DataOps and Data Stewardship functions within the Data organization. This role ensures end-to-end delivery excellence of the cloud-native data platform – spanning data ingestion, transformation, modeling, and operations – to enable reliable, high-quality, and self-service analytics across business domains.

Responsibilities:

• Team Leadership: Recruit, mentor, and lead a hybrid team of data engineers and stewards across Singapore, Malaysia and India, establishing in-house technical leadership and delivery ownership.

• Data Engineering Delivery: Oversee design, development, and optimization of ELT/ETL pipelines and data models, ensuring scalable, reusable, and cost-efficient workflows.

• Data Quality & Stewardship: Institutionalize stewardship processes — define ownership models, implement DQ monitoring, and drive remediation workflows with cross-functional data users.

• Operational Excellence: Manage daily pipeline operations, SLA compliance, and production issue resolution with strong root-cause analysis and continuous improvement.

• Technical Governance: Set engineering standards for observability, RBAC, cost tagging, and CI/CD practices.

• Collaboration & Enablement: Enable self-service analytics by curating trusted datasets and modeled views, working with BI and business teams.

Requirements:
• 8–12 years of experience in cloud-native data engineering, with strong architecture and delivery experience on AWS.

• Proven leadership of cross-functional and hybrid engineering teams, including vendor-augmented resources.

• Experience partnering with BI and business teams to design modelled datasets and enable self-service analytics.

• Deep hands-on technical expertise, including: Snowflake: schema design, Streams/Tasks, Stored Procedures, UDFs, RBAC, performance tuning, Cortex AI, Streamlit, cost monitoring.

• Airflow or similar data orchestration tools: orchestration, scheduling, dependency management, and observability.

• Python and SQL: pipeline scripting, transformation logic, and data validation.

• ELT/ETL frameworks: Airbyte, Fivetran, and custom connector development.

• AWS services: S3 (data lake structures and archival), Lambda, KMS, Transfer Family, CloudWatch, Sagemaker.

• Demonstrated success delivering medallion architecture (Bronze/Silver/Gold) and enabling self-service data use cases.

• Experience building data quality frameworks, stewardship policies, and data lineage tracking across enterprise datasets.

• Familiarity with machine learning integration using platforms like AWS SageMaker.

• Proven ability to troubleshoot complex data issues, lead root-cause analysis, and ensure production stability.

• Track record of transitioning delivery ownership from vendors to internal teams while maintaining quality and velocity.