AWS Snowflake Architect (Hybrid, Markham, CAD Salary)

HR POD Careers; Markham; On-site; CAD 140,000 - 180,000; Full time

Job summary

HR POD Careers in Ontario seeks a Senior Data Architect with 8+ years of experience in Data Architecture, Data Engineering, or enterprise data platform development. You will design and implement scalable Snowflake-based architectures and cloud data solutions.

You will lead ETL/ELT pipelines with AWS Glue, Apache Airflow, and dbt, define data models and standards, and partner with engineering and analytics teams to deliver secure, high-performance data systems.

Qualifications

• 8+ years of experience in Data Architecture, Data Engineering, or enterprise data platform development.
• Strong expertise in Snowflake architecture, data warehousing, and enterprise-scale data platforms.
• Hands-on experience with AWS cloud services and designing scalable, cloud-based data solutions.
• Strong experience with AWS Glue, Apache Airflow, and dbt for data integration and transformation.
• Expertise in data modeling, ETL/ELT architecture, data pipelines, and data integration patterns.
• Strong proficiency in SQL and Python for data processing and automation.
• Experience designing secure, scalable, and high-performance data architectures.
• Experience within the insurance domain is preferred.

Responsibilities

• Design and implement scalable enterprise data architectures using Snowflake and AWS.
• Develop architectures for data ingestion, transformation, integration, and consumption across enterprise systems.
• Design and optimize ETL/ELT pipelines using AWS Glue, Apache Airflow, dbt, and related technologies.
• Define data models, architecture standards, integration patterns, and technical best practices.
• Optimize Snowflake environments for performance, scalability, security, and cost efficiency.
• Collaborate with engineering, analytics, and business teams to translate data requirements into effective technical solutions.
• Provide technical leadership and guidance to data engineering teams throughout the implementation process.
• Ensure data solutions meet enterprise standards for security, governance, reliability, and performance.

Skills

Snowflake architecture

Data modeling

ETL/ELT design

AWS cloud

Airflow

dbt

SQL

Python

Data warehousing

Insurance domain knowledge

Job description

• 8+ years of experience in Data Architecture, Data Engineering, or enterprise data platform development.
• Strong expertise in Snowflake architecture, data warehousing, and enterprise-scale data platforms.
• Hands-on experience with AWS cloud services and designing scalable, cloud-based data solutions.
• Strong experience with AWS Glue, Apache Airflow, and dbt for data integration and transformation.
• Expertise in data modeling, ETL/ELT architecture, data pipelines, and data integration patterns.
• Strong proficiency in SQL and Python for data processing and automation.
• Experience designing secure, scalable, and high-performance data architectures.
• Experience within the insurance domain is preferred.

Requirements:

• 8+ years of experience in Data Architecture, Data Engineering, or enterprise data platform development.
• Strong expertise in Snowflake architecture, data warehousing, and enterprise-scale data platforms.
• Hands-on experience with AWS cloud services and designing scalable, cloud-based data solutions.
• Strong experience with AWS Glue, Apache Airflow, and dbt for data integration and transformation.
• Expertise in data modeling, ETL/ELT architecture, data pipelines, and data integration patterns.
• Strong proficiency in SQL and Python for data processing and automation.
• Experience designing secure, scalable, and high-performance data architectures.
• Experience within the insurance domain is preferred.

Responsibilities:

• Design and implement scalable enterprise data architectures using Snowflake and AWS.
• Develop architectures for data ingestion, transformation, integration, and consumption across enterprise systems.
• Design and optimize ETL/ELT pipelines using AWS Glue, Apache Airflow, dbt, and related technologies.
• Define data models, architecture standards, integration patterns, and technical best practices.
• Optimize Snowflake environments for performance, scalability, security, and cost efficiency.
• Collaborate with engineering, analytics, and business teams to translate data requirements into effective technical solutions.
• Provide technical leadership and guidance to data engineering teams throughout the implementation process.
• Ensure data solutions meet enterprise standards for security, governance, reliability, and performance.