ETL Data Engineer

Senior Data Engineer – Azure / Python ETL Modernization Remote (U.S.) with Minimal travel (2–3x per year) Overview 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. Core Responsibilities 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) Required Skills 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 Preferred Experience 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 Why This Role Is Different 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 {"@context":"http://schema.org","@type":"JobPosting","baseSalary":null,"datePosted":"2026-06-22","validThrough":"2027-06-22","description":"Senior Data Engineer – Azure / Python ETL Modernization

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

Overview

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.

Core Responsibilities

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)

Required Skills

• 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

Preferred Experience

• 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

Why This Role Is Different

• 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

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