Principal Data Engineer – Databricks | Spark | Delta Lake | PySpark | Data Lakehouse | AWS/Azure
Location: Toronto
Work Model: Onsite (4 days/week)
Key Requirements
- 12–18 years of overall Data Engineering experience.
- 8+ years of experience with Enterprise Data Warehouse and Data Lake platforms.
- 5+ years of hands‑on experience with Databricks and Apache Spark at scale.
- Strong experience modernizing legacy Cloudera platforms (CDH/CDP, Hive, HBase, Impala, Spark) to Databricks Lakehouse.
- Experience redesigning ingestion, transformation, and consumption patterns from HDFS-based architecture to cloud object storage and Delta Lake.
- Experience refactoring legacy Hive/Impala logic into PySpark and Spark SQL ELT pipelines.
- Experience ensuring data reconciliation, audit integrity, and consistency during migration.
- Experience designing and governing Enterprise Data Warehouse and Data Lake/Lakehouse architectures.
- Experience implementing layered architectures including:
- Raw/Landing Layer
- Curated/Conformed Layer
- Semantic/Consumption Layer
- Experience modernizing traditional Enterprise Data Warehouse platforms into scalable Lakehouse architectures.
- Strong experience with finance and risk data models, including:
- General Ledger
- Sub‑ledger
- Financial Hierarchies
- Credit Risk Models
- Liquidity Risk Models
- Market Risk Models
- Experience enabling reporting use cases including aggregation, drill‑down, and drill‑back capabilities.
- Experience building and managing semantic/consumption layers for BI, reporting, and analytics.
- Ability to define business metrics, dimensions, hierarchies, and KPIs.
- Experience with Databricks SQL, Delta Tables, and dbt or similar frameworks.
- Strong experience developing and optimizing large‑scale data pipelines using:
- PySpark
- Spark SQL
- Delta Lake
- Experience implementing Medallion Architecture:
- Bronze Layer
- Silver Layer
- Gold Layer
- Experience optimizing workloads using Z‑ORDER, OPTIMIZE, caching, and cluster configurations.
- Experience implementing data governance, data quality frameworks, reconciliation controls, and exception handling.
- Experience establishing data lineage and metadata management.
- Knowledge of 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
- Azure DevOps
- Familiarity with orchestration tools such as:
- Apache Airflow
- Databricks Workflows
- Experience with dbt is a plus.
- Ability to act as a technical authority and lead architecture decisions.
- Experience mentoring senior engineers and establishing engineering standards.
- Strong stakeholder management skills with finance, risk, analytics, and governance teams.
- Ability to translate complex data structures into business‑ready insights.
Nice to Have
- Experience in Banking, Financial Services, Insurance (BFSI), Capital Markets, or regulatory reporting.
- Exposure to:
- SAP Finance
- Oracle Financials
- SAP S/4HANA
- Experience supporting AI/ML workloads.
- Databricks or cloud certifications.
Key Responsibilities
- Lead Cloudera to Databricks transformation initiatives.
- Design and implement enterprise Data Lakehouse and Data Warehouse solutions.
- Build scalable, high‑performance data pipelines and modern data architectures.
- Drive data modernization, governance, quality, and security initiatives.
- Support regulatory, management, and analytical reporting platforms.
- Provide technical leadership, mentor engineering teams, and establish best practices.
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