Key Requirements

- 12–18 years of overall data engineering experience
- 8+ years of experience in enterprise Data Warehouse and Data Lake platforms
- 5+ years of hands‑on experience with Databricks and Spark at scale
- Solid experience in modernizing legacy Cloudera platforms (CDH/CDP, Hive, HBase, Impala, Spark) to Databricks Lakehouse
- Redesign ingestion, transformation, and consumption patterns from HDFS‑based architecture to cloud object storage and Delta Lake
- Refactor legacy Hive/Impala logic into PySpark and Spark SQL ELT pipelines
- Ensure data reconciliation, audit integrity, and consistency during migration
- Design and govern enterprise Data Warehouse and Data Lake/Lakehouse architectures
- Implement layered architecture including Raw/Landing, Curated/Conformed, and Semantic/Consumption layers
- Modernize traditional EDW platforms into scalable lakehouse architectures
- Strong experience in finance and risk data models including General Ledger, Sub‑ledger, financial hierarchies, and risk exposure models (credit, liquidity, market risk)
- Enable reporting use cases including aggregation, drill‑down, and drill‑back capabilities
- Build and manage semantic/consumption layers for BI, reporting, and analytics
- Define business metrics, dimensions, hierarchies, and KPIs
- Experience with Databricks SQL, Delta tables, and dbt or similar frameworks
- Develop and optimize large‑scale data pipelines using PySpark,



Spark SQL, and Delta Lake
- Implement Medallion architecture (Bronze, Silver, Gold layers)
- Optimize workloads using Z‑ORDER, OPTIMIZE, caching, and cluster configurations
- Implement data governance, data quality frameworks, reconciliation controls, and exception handling
- Establish data lineage and metadata management
- Ensure data security, access control, and compliance standards
- Experience with cloud platforms such as AWS or Azure
- Experience with CI/CD pipelines using Git, Terraform, Jenkins, or Azure DevOps
- Familiarity with orchestration tools such as Airflow or Databricks Workflows
- Experience with dbt is a plus
- Act as a technical authority and lead architecture decisions
- Mentor and guide senior engineers and establish engineering standards
- Strong stakeholder management with finance, risk, analytics, and governance teams
- Ability to translate complex data structures into business‑ready insights

Nice to Have

- Experience in BFSI, Capital Markets, or regulatory reporting
- Exposure to SAP Finance, Oracle Financials, or S/4HANA
- Experience supporting AI/ML workloads
- Databricks or cloud certifications

Impact

- Lead Cloudera to Databricks transformation initiatives
- Shape enterprise finance and risk data platforms
- Support regulatory, management, and analytical reporting systems

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📌 Databricks Engineer (Toronto)
🏢 PeoplePilot
📍 Toronto