Title: Azure Data Lead

Location: NYC (3 days onsite)

Skills: Azure Data lead - Python, Pyspark, Databricks, ADF, Data Lake

Introduction

The Azure Data Lead will be responsible for leading the modernization and migration of existing Python object-oriented applications into scalable PySpark and Spark SQL data-processing solutions on Azure Databricks. This role requires a strong blend of software engineering, data engineering, cloud architecture, and performance optimization.

Responsibilities

• 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 (partitioning, bucketing, caching, broadcast joins, Adaptive Query Execution, Delta optimization) and benchmark converted applications against original Python implementations.

Requirements

Core Skills:

• 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: 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

The ideal candidate for this role will be a senior data engineering leader with proven delivery of large-scale Databricks modernization programs. The expected outcome is to have existing Python applications converted into scalable, cost-efficient, enterprise-grade data solutions on Azure Databricks with proven performance parity.