Vp engineering Jobs in Saanich, BC

Position Overview

We are seeking a Senior Data Modeler to support our clients enterprise data and analytics initiatives. The successful candidate will be responsible for designing, developing, and optimizing scalable data models to support business intelligence, reporting, analytics, and operational data requirements.

This role requires strong expertise in dimensional modeling, Data Vault 1.0/2.0, medallion architecture, and Snowflake. The ideal candidate will work closely with data architects, data engineers, and business stakeholders to develop high-quality data models that support modern cloud-based data platforms.

Duration - 1 year

Work Mode - Remote

Start - ASAP

Key Responsibilities

• Design and develop conceptual, logical, and physical data models to support enterprise analytics, reporting, and operational requirements.
• Apply advanced data modeling methodologies, including dimensional modeling, Data Vault 1.0 and 2.0, and medallion architecture (bronze, silver, and gold layers).
• Develop star schemas, snowflake schemas, fact tables, and dimension tables to support analytical workloads.
• Design Data Vault structures, including hubs, links, and satellites, following Data Vault 2.0 best practices.
• Collaborate with data architects, data engineers, business analysts, and stakeholders to gather requirements and translate business needs into scalable data models.
• Create and maintain comprehensive data modeling documentation, including entity-relationship diagrams (ERDs), data dictionaries, metadata definitions, and source-to-target mapping specifications.
• Design and optimize data models within Snowflake, considering query performance, scalability, maintainability, and cost efficiency.
• Partner with data engineering teams to ensure accurate implementation of data models across cloud-based data pipelines.
• Review, validate, and enhance existing data models to align with evolving business and technical requirements.
• Support data quality, governance, and consistency across enterprise data environments.

Required Skills and Experience

• Extensive hands-on experience in enterprise data modeling, including conceptual, logical, and physical data model design.
• Strong expertise in dimensional modeling, including star schemas, snowflake schemas, fact tables, and dimension design.
• Proven experience designing and implementing Data Vault 1.0 and 2.0 models, including hubs, links, satellites, and associated modeling principles.
• Solid understanding of medallion architecture, including bronze, silver, and gold data layers within modern cloud data platforms.
• Strong hands-on experience with Snowflake, including:
• Micro-partitioning and data organization.
• Clustering keys and query optimization.
• VARIANT data types and semi-structured data modeling.
• Performance optimization and scalable data design.
• Advanced SQL skills, including working with complex queries and structured and semi-structured datasets.
• Experience translating complex business requirements into efficient, scalable, and maintainable data models.
• Strong understanding of data warehouse architecture, data integration, and modern data engineering practices.
• Excellent analytical, problem-solving, communication, and stakeholder collaboration skills.

Nice to Have

• Experience with data modeling and documentation tools such as ERwin, dbt, or Lucidchart.
• Understanding of data governance, metadata management, master data management (MDM), and data quality principles.
• Experience working with cloud-based data platforms, particularly Snowflake on Microsoft Azure.
• Knowledge of Snowflake performance tuning, workload optimization, and cost management.
• Previous experience supporting enterprise-scale data transformation, modernization, or analytics initiatives.
• Experience working in the airline, transportation, or other large enterprise environments is an asset.

Ideal Candidate Profile

The ideal candidate is a hands-on Data Modeler with strong technical expertise in Snowflake, dimensional modeling, and Data Vault 2.0. They should be comfortable working in complex enterprise data environments, collaborating with cross-functional teams, and designing scalable data structures that support both current and future business requirements.