Snowflake Data Architect

Key Responsibilities

• Define and lead the architecture and design of enterprise data platforms using Snowflake on AWS.
• Architect scalable data ingestion frameworks for integrating multiple source systems into the cloud data platform.
• Design and govern data transformation frameworks using DBT.
• Define and enforce data modelling standards including dimensional modelling, star schema, and enterprise data models.
• Lead architecture reviews and solution design discussions for new data initiatives.
• Optimize Snowflake performance, workload management, and cost governance.
• Establish data governance frameworks including access control, data security, and compliance standards.
• Design and support AI/ML-ready data architecture for advanced analytics and predictive modelling.
• Provide architectural guidance to data engineering, BI, and analytics teams.
• Design architecture to support data consumption for reporting systems, operational applications, and analytics platforms.
• Implement automation, orchestration, and scalable pipeline frameworks using tools such as Apache Airflow.
• Collaborate with business stakeholders and technical teams to align the data platform with enterprise data strategy.
• Support hospitality analytics use cases, including guest behaviour analysis, booking trends, revenue analytics, and operational reporting.

Required Technical Skills

Data Platform

• Strong expertise in
• Snowflake
• Deep knowledge of Snowflake architecture, performance tuning, data sharing, security, and workload optimization.

Cloud Platform

Strong experience with Amazon Web Services, including:

• S3
• IAM
• AWS Glue
• Lambda
• CloudWatch

Data Transformation

• Strong experience with
• DBT
• for enterprise-scale data modelling, testing, and transformation pipelines.

Programming / Query

• Strong expertise in SQL for data transformation and performance optimization
• Python (preferred) for automation and data engineering tasks.

Data Engineering

• Enterprise ETL / ELT pipeline architecture
• Data warehousing and enterprise data modelling
• Dimensional modelling (Star Schema, Snowflake Schema)
• Data pipeline scalability and reliability design.

AI / Data Science Exposure

• Experience supporting AI/ML data pipelines and data preparation for machine learning models.
• Understanding of predictive analytics, recommendation engines, and customer behaviour analytics.
• Ability to design AI-ready data platforms for future analytics use cases.

Preferred Skills

• Experience with Apache Airflow for pipeline orchestration.
• Knowledge of CI/CD pipelines, DevOps, and Git-based development workflows.
• Experience with data governance, metadata management, and enterprise data catalog tools.
• Experience with BI tools such as
• Tableau
• Microsoft Power BI.
• Domain experience in hospitality, travel, or hotel systems, including reservation systems, guest analytics, and operational reporting.