ETL Data Engineer

Job Description

Job Description

Senior Data Engineer – Azure / Python ETL Modernization

Remote (U.S.) with Minimal travel (2–3x per year)

We’re hiring a Senior Data Engineer to lead enterprise ETL modernization initiatives, transitioning legacy data pipelines (e.g., Informatica, on-prem data warehouses) into modern Azure-based, Python-driven data platforms.

This is a hands-on engineering role focused on building scalable data pipelines, refactoring legacy logic into Python/PySpark, and delivering production-grade data solutions that support analytics, reporting, and downstream data use cases.

The right candidate will have a strong background in Python-based data engineering, Azure data services, and experience modernizing legacy ETL environments.

ETL Modernization (Primary Focus)

• Refactor and migrate legacy ETL pipelines (e.g., Informatica) into Python/PySpark-based pipelines
• Translate business logic into scalable, code-driven transformations (not tool-based ETL)
• Support large-scale migration from on-prem data warehouses to Azure

Data Pipeline Engineering

• Build and maintain pipelines using Azure Data Factory, Synapse Pipelines, and/or Databricks
• Develop reusable, parameter-driven frameworks for ingestion and transformation
• Implement ELT patterns leveraging SQL pushdown and distributed processing

Python & Spark Development

• Develop and optimize PySpark jobs for large-scale data processing
• Write clean, testable Python code for transformation, orchestration, and data quality
• Integrate with APIs and external data sources

Data Architecture & Modeling

• Implement lakehouse architecture (ADLS Gen2, Delta Lake, Parquet)
• Design dimensional models (star/snowflake) for analytics use
• Handle SCD (Type 1/2), CDC, and complex transformation logic

Platform & DevOps

• Build CI/CD pipelines using Azure DevOps (YAML, Terraform/Bicep)
• Implement monitoring, logging, and alerting (Azure Monitor, Log Analytics)
• Ensure security and access controls (RBAC, Key Vault, networking)

• Strong hands-on experience with Python for data engineering (non-negotiable)
• Solid experience with PySpark / Spark-based processing frameworks
• Experience with Azure Data Factory, Synapse, or Databricks
• Advanced SQL (complex transformations, optimization, performance tuning)
• Experience working with modern data lakes (ADLS Gen2, Delta Lake)
• Experience with ETL modernization or legacy system migration
• Familiarity with CI/CD and DevOps practices in data engineering

• Background migrating Informatica or similar ETL tools into Python-based frameworks
• Experience with large enterprise data warehouse environments (Teradata, SQL Server, Oracle)
• Exposure to regulated environments (healthcare, financial, etc.)
• Snowflake experience is a plus

• Focus on real modernization work, not legacy ETL maintenance
• Heavy emphasis on Python-first data engineering
• Opportunity to influence architecture and engineering standards
• Long-term, high-impact enterprise data platform