Azure Senior Data Lead leads modernization of Python
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Role details
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Job description
Azure Senior Data Lead leads modernization of Python applications into scalable PySpark solutions on Azure Databricks. Role Purpose
Lead the modernization and migration of existing Python object-oriented applications into scalable PySpark and Spark SQL data-processing solutions on Azure Databricks - bringing a strong blend of software engineering, data engineering, cloud architecture, and performance optimization., * Analyze existing Python OOP applications and redesign single-node processing logic for distributed Spark execution.
- Design, develop, and deploy enterprise-scale data pipelines on Azure Databricks; build reusable PySpark frameworks and utility modules.
- Implement Delta Lake solutions using the Bronze Silver Gold architecture.
- Build robust ETL/ELT pipelines with Azure Data Factory, ADLS Gen2, and Azure Synapse Analytics.
- Implement data quality, reconciliation, validation, and monitoring frameworks.
- Optimize Spark jobs using partitioning, bucketing, caching, broadcast joins, Adaptive Query Execution, and Delta optimization.
- Benchmark converted applications against original Python implementations.
Requirements
Azure Senior Data Lead with Azure Data Factory (ADF), Azure Databricks, SQL, Oracle PL/SQL, Python
Location: New York, NY
Experience: 7-12 Years
Primary Skills: Azure Data Factory (ADF), Azure Databricks, SQL, Oracle PL/SQL, Python
Certifications: Relevant certifications in Azure Data Factory, Azure Databricks, SQL, Oracle, or Python would be a plus., * Python (expert), OOP, and advanced Python design patterns
- PySpark, Spark SQL, and SQL
- Azure Databricks, Azure Data Factory, ADLS Gen2
- Apache Spark, Delta Lake, Data Lakehouse architecture, distributed computing
Must-have Skills
- Python
- Azure Databricks
- Azure Data Factory (ADF)
- MS SQL
- Oracle PL/SQL
Good to Have
- PySpark
- Certifications in Azure Data Factory, Azure Databricks, SQL, Oracle, or Python
Experience & Expected Outcome
Senior data engineering leader with proven delivery of large-scale Databricks modernization programs.
Expected outcome: Existing Python applications converted into scalable, cost-efficient, enterprise-grade data solutions on Azure Databricks with proven performance parity.
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