Data Solution Architect
Team: Data
Location: Toronto, Vancouver
Workplace Type: hybrid
RESPONSIBILITIES:
- Design and architect modern, scalable, and composable data platforms across multi-cloud environments (AWS, GCP, Azure), supporting real-time analytics, AI/ML, and customer experience use cases.
- Assess client data ecosystems and create strategic modernization roadmaps—from on-prem to cloud migrations, to enabling unified customer data and activation.
- Create and enforce architectural standards, governance models, and reference architectures across data lakehouse, mesh, and medallion patterns.
- Evaluate and recommend data storage and compute technologies (e.g., BigQuery, Snowflake, Redshift, Delta Lake, Apache Iceberg) based on performance, scalability, cost, and security trade-offs.
- Design robust, scalable ETL/ELT pipelines using both batch and streaming tools (e.g., dbt, Airflow, Kafka, Kinesis, Pub/Sub), and optimize data flows for speed and reliability.
- Define and enforce data governance frameworks, including RBAC/ABAC models, lineage tracking, metadata management, and compliance (GDPR, CCPA, SOC2).
- Develop architectural guidance for AI/ML data pipelines, including the design of feature stores, real-time inference pipelines, and MLOps alignment.
- Recommend tools and practices for data cataloging, quality, observability, and security, leveraging platforms such as Collibra, DataHub, or Apache Atlas.
- Provide strategic advisory to business and technical stakeholders, aligning data architecture decisions with broader digital transformation and ROI goals.
- Create clear, client-facing deliverables: reference architectures, integration strategies, migration plans, and governance models.
REQUIREMENTS:
- 10+ years of experience in data architecture, platform engineering, or solution architecture within enterprise or consulting environments.
- Proven experience architecting and implementing large-scale data modernization programs using Snowflake (required) and/or GCP, AWS, Azure.
- Snowflake SnowPro Advanced Architect certification (nice to have GCP Professional Data Engineer or Architect certification)
- Expertise in data modeling (dimensional, Data Vault), schema evolution, and building systems to handle both structured and unstructured data.
- Strong understanding of data lake, data warehouse, lakehouse, and data mesh architectures, and when to apply each.
- Experience designing high-performance, scalable pipelines using dbt, Apache Airflow, Kafka, Kinesis, Pub/Sub, or similar tools.
- Knowledge of Terraform for Infrastructure as Code (IaC) in data environments.
- Experience integrating CDPs and MarTech/AdTech platforms for real-time personalization.
- A consultative mindset — you’re comfortable assessing ambiguous environments, providing strategic guidance, and influencing stakeholders toward long-term, scalable solutions.
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