Principal Databricks Data Engineer
Summary
Principal Data Engineer leading the migration from Cloudera to Databricks, building scalable Lakehouse architectures for finance and risk analytics using PySpark, Delta Lake, and Medallion patterns.
Principal Databricks Data Engineer
Experience Required: 12–18 Years
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 including:
- CDH/CDP
- Hive
- HBase
- Impala
- Spark
- Modernize Cloudera platforms to Databricks Lakehouse architecture
- Redesign ingestion, transformation, and consumption patterns from HDFS‑based architecture to Cloud Object Storage and Delta Lake
- Refactor legacy Hive and 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 data architecture including:
- Raw / Landing Layer
- Curated / Conformed Layer
- Semantic / Consumption Layer
- Modernize 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
- Liquidity Risk
- Market Risk
- Enable reporting capabilities including:
- Aggregation
- Drill‑down
- Drill‑back
- Build and manage semantic and consumption layers for BI, reporting, and analytics
- Define business metrics, dimensions, hierarchies, and KPIs
- Experience with:
- Databricks SQL
- Delta Tables
- dbt or similar frameworks
- Develop and optimize large‑scale data pipelines using:
- PySpark
- Spark SQL
- Delta Lake
- Implement Medallion Architecture including:
- Bronze Layer
- Silver Layer
- Gold Layer
- Optimize workloads using:
- Z‑ORDER
- OPTIMIZE
- Caching
- Cluster Configuration Tuning
- Implement:
- Data Governance
- Data Quality Frameworks
- Reconciliation Controls
- Exception Handling
- Establish data lineage and metadata management
- Ensure data security, access control, and compliance standards
- Experience with AWS or Azure cloud platforms
- Experience with CI/CD pipelines using:
- Git
- Terraform
- Jenkins
- Azure DevOps
- Familiarity with:
- Apache Airflow
- Databricks Workflows
- Experience with dbt is an advantage
- Act as a technical authority and lead enterprise architecture decisions
- Mentor senior engineers and establish engineering standards
- Collaborate with finance, risk, analytics, and governance stakeholders
- Translate complex data structures into business‑ready insights
Nice‑to‑Have Skills
- Experience in BFSI, Capital Markets, or Regulatory Reporting
- Exposure to:
- SAP Finance
- Oracle Financials
- SAP S/4HANA
- Experience supporting AI/ML workloads
- Databricks and Cloud Certifications
Key Responsibilities
- Lead Cloudera to Databricks transformation initiatives
- Shape enterprise finance and risk data platforms
- Support regulatory, management, and analytical reporting systems
Essential Skills
- Databricks
- Apache Spark
- PySpark
- Spark SQL
- Delta Lake
- Lakehouse Architecture
- Cloudera (CDH/CDP)
- Hive
- HBase
- Impala
- Data Warehouse & Data Lake
- Medallion Architecture
- Databricks SQL
- dbt
- AWS / Azure
- Airflow / Databricks Workflows
- CI/CD
- Terraform
- Jenkins
- Git
- Finance & Risk Data Models
- Enterprise Data Architecture