Description

Key Responsibilities

• Design, develop, and maintain enterprise ETL pipelines supporting large-scale data platforms
• Build and optimize Python-based data transformation logic (data A → B implemented in Python)
• Develop scalable data processing solutions using Spark and Databricks
• Support enterprise analytics and regulated reporting initiatives
• Implement data validation, reconciliation, and audit-traceable pipelines
• Write and optimize complex SQL across enterprise data platforms (Snowflake, Oracle, SQL Server, Teradata)
• Participate in legacy ETL modernization initiatives (e.g., Informatica or shell to Python conversions)
• Support cloud-based data architectures within Azure environments
• Collaborate with architects, analysts, QA, and reporting teams to ensure data quality and accuracy
• Participate in CI/CD, code reviews, and source control using Azure DevOps and GitHub
• Support production operations, incident resolution, and root-cause analysis

Required Qualifications

• 5+ years of enterprise data engineering experience
• 5+ years of hands-on ETL development (Informatica PowerCenter, Azure Data Factory, or similar tools)
• 5+ years of Python development focused on data engineering and transformation logic
• 3+ years of Spark-based processing (Databricks or equivalent)
• Strong SQL expertise across large relational databases
• Experience working in regulated, audit-sensitive environments
• Strong analytical, troubleshooting, and problem-solving skills
• Bachelor’s degree or higher in Computer Science, Engineering, Analytics, or related field

Preferred Qualifications

• Experience supporting large enterprise data warehouse environments
• Healthcare or public-sector data experience preferred
• Experience with data quality frameworks and reconciliation processes
• Scripting experience (PowerShell or Bash)
• Experience designing or consuming REST APIs
• Cloud-based data engineering experience in Azure
• Azure data or analytics certifications

Key Skills

Education

Any Gradute