Senior Data Architect

We are looking for a Senior Data Architect to design and implement the end-to-end data platform for APEX, integrating call center telephony and quality data from the Genesys A3S Snowflake database and Verint Workforce Management (WFM) feeds. The role will focus on building a scalable, reliable, and auditable data architecture using Databricks, Delta Lake, and a Bronze/Silver/Gold (Medallion) architecture, delivering curated data through a Kimball-style dimensional reporting layer.

The ideal candidate combines deep hands-on expertise in data warehouse modeling, lakehouse architecture, SQL, and production-grade data ingestion with the ability to make sound architectural decisions around data quality, performance, scalability, and operational resilience. You will define ingestion strategies, incremental loading patterns, batch controls, and data integrity standards while supporting the transition from existing CCARS pipelines to the new platform through a controlled parallel-run and decommissioning process.

This role is suited to a Senior or Lead Data Architect with strong technical ownership who can translate complex business and reporting requirements into robust data solutions and clearly communicate architectural decisions to both engineering teams and business stakeholders.

Working Model: This is a hybrid role requiring 2 days per week in a downtown Toronto office.

Responsibilities:

• Design and implement end-to-end Bronze/Silver/Gold data architecture for Genesys A3S Snowflake and Verint data sources, including data ingestion, transformation, validation, and consumption.
• Develop dimensional data models using Kimball principles, including star schemas, fact and dimension tables, grain definition, conformed dimensions, and SCD Type I/II, supporting sales, contact center telephony, agent activity, quality evaluations, and operational reporting.
• Build and optimize scalable data pipelines using Databricks, Delta Lake, Python, PySpark, Spark SQL, and T-SQL, applying incremental loading, watermarks, checkpoints, ACID transactions, partitioning, Z-ordering, and query optimization.
• Integrate Snowflake sources, vendor flat files, SFTP transfers, and REST APIs, implementing secure connectivity, pagination, rate-limit handling, asynchronous processing, schema evolution, data normalization, and source-to-target reconciliation.
• Establish data quality, integrity, and audit controls, including deduplication, referential integrity, row counts, checksum/hash comparisons, late-arriving data handling, and detection and remediation of missing records, data drift, and schema changes.
• Design and maintain reliable orchestration, monitoring, and recovery processes using Azure Data Factory, Databricks Workflows, or equivalent tools; optimize pipeline performance, scalability, and cost while supporting high-volume processing and production troubleshooting.