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.
Requirements
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.
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.
