Position: Data Architect (Databricks &

- Medallion) Location: Hybrid- Toronto, ON Job type: Full-time/Permanent hiring The Senior Data Architect will design the end-to-end data platform for APEX, ingesting call center telephony and quality data from Genesys A3S Snowflake and Verint WFM feeds, and delivering it through a Bronze/Silver/Gold (Medallion) architecture into a Kimball-style reporting layer. This role requires deep hands-on expertise in dimensional modeling, Databricks/Delta Lake, Spark SQL, and API-driven ingestion to deliver reliable, production-grade analytics for sales and telephony reporting. Roles &

Responsibilities Medallion Architecture: Design Bronze/Silver/Gold data flow for Genesys A3S Snowflake and Verint sources.

Ingestion Strategy: Define ingestion cadence and watermarking strategy per source, based on true data freshness.

Star Schema Design: Design Gold-layer star schema (facts, dimensions) to support sales and telephony reporting. Data Integrity &

- Idempotency: Define data integrity checks, batch control, idempotency, and failure-recovery patterns for each ingestion pipeline.

Architecture Documentation: Document architecture decisions and trade-offs for both engineering and business audiences. Parallel-Run &

- Decommission: Partner with the existing CCARS pipeline team during the parallel-run and decommission phases.

Dimensional Modeling: Lead Kimball star schema, fact/dimension design, grain definition, and SCD Type I/II modeling.

Databricks Optimization: Tune Databricks workloads for cost and performance at scale (cluster sizing, job clusters vs. all-purpose, Photon).

SQL Expertise:



Write and review production-grade transformation logic in both T-SQL (SQL Server) and Spark SQL.

Data Integrity Enforcement: Enforce referential integrity, deduplication, reconciliation, validation/audit checks, and late-arriving data handling.

API Integration at Scale: Consume and design around REST APIs at scale (pagination, rate limiting, async job patterns, per-entity fan-out APIs). Multi-Source Ingestion: Ingest from external Snowflake sources, vendor exports (flat files, SFTP, APIs), normalizing non-standardized formats during landing. High-Volume Pipeline Design: Design pipelines handling millions of records/day, with backpressure, retry, and failure isolation. Orchestration &

- Scheduling: Work with Azure Data Factory, Databricks Workflows, and job scheduling (AutoSys or similar).

Azure Ecosystem: Work with Azure ecosystem tools (Databricks, ADF, SQL Managed Instance, Key Vault) and Python/PySpark for pipeline/transformation logic.

Required Technical Competencies Experience: 10 years designing data warehouse and lakehouse solutions, with at least 5 years of hands-on Databricks/Delta Lake and Snowflake/API-driven ingestion experience.

Dimensional

Modeling (Primary): Deep, hands-on Kimball star schema, fact/dimension design, grain definition, SCD Type I/II expertise.

Medallion

Architecture (Primary):



Proven experience designing Bronze/Silver/Gold Lakehouse architectures. Databricks &

- Delta Lake (Primary): Robust hands-on expertise with Delta tables, ACID transactions, Z-ordering, optimize/vacuum, and workload tuning for cost/performance at scale. SQL (Primary): Expert-level T-SQL and Spark SQL — complex joins, window functions, query tuning, indexing, partitioning strategy.

Data Integrity: Strong track record enforcing referential integrity, deduplication, reconciliation, validation/audit checks, and late-arriving data handling. Databases (In and Out): Deep hands-on knowledge of relational databases (SQL Server preferred), understanding OLTP vs. OLAP design differences.

API Integration: Experience consuming and designing around REST APIs at scale (pagination, rate limiting, async job patterns). Multi-Source Ingestion: Experience integrating Genesys Snowflake, vendor exports (Verint-style flat files, SFTP, APIs), with schema drift tolerance. High-Volume Pipelines: Track record designing pipelines handling millions of records/day, with backpressure, retry, and failure isolation design. Programming &

- Tooling: Python or PySpark for pipeline/transformation logic; familiarity with Azure ecosystem (Databricks, ADF, SQL Managed Instance, Key Vault).

Orchestration: Experience with Azure Data Factory, Databricks Workflows, or equivalent; job scheduling (AutoSys or similar a plus).

Preferred Domain: Prior experience with Genesys Cloud or Verint data models, contact center/telephony domain knowledge, and parallel-run migration strategies.

📌 Data Architect (Databricks) (Ontario)
🏢 Ku0026K Global Talent Solutions
📍 Ontario