Azure Data lead

Shrive Technologies; New York (NY); On-site; USD 170,000 - 250,000; Full time

Job summary

Shrive Technologies seeks an Azure Senior Data Lead to guide the modernization of Python applications into scalable PySpark solutions on Azure Databricks. You will blend software engineering, data engineering, cloud architecture, and performance optimization to deliver enterprise-grade data platforms.

The role requires leading design and implementation of Delta Lake architectures, data pipelines, and quality frameworks across Azure services, with emphasis on scalable, cost-efficient outcomes and

Qualifications

• Senior data engineering leader with proven delivery of large-scale Databricks modernization programs.
• Must convert Python applications into scalable PySpark and Spark SQL data-processing solutions on Azure Databricks.

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 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, AQE, Delta optimization) and benchmark against originals.

Skills

Python

OOP

advanced Python design patterns

PySpark

Spark SQL

SQL

Azure Databricks

Azure Data Factory

ADLS Gen2

Apache Spark

Delta Lake

Data Lakehouse architecture

distributed computing

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.

Key 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.

Core Skills

• Python (expert)
• OOP
• advanced Python design patterns
• PySpark
• Spark SQL
• SQL
• Azure Databricks
• Azure Data Factory
• ADLS Gen2
• Apache Spark
• Delta Lake
• Data Lakehouse architecture
• distributed computing

Must-have (per requisition)

• 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.