Principal Data Engineer – Databricks

Principal Data Engineer – Databricks | Spark | Delta Lake | PySpark | Data Lakehouse | AWS/Azure

Job Description

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.