Hybrid Data Engineer - ETL, Python & Databricks

MMC; Louisville (KY); Hybrid; USD 90,000 - 140,000; Full time

Job summary

Marsh in Louisville, KY is seeking a Data Engineer to join the Applications Development team. This hybrid role requires working at least three days a week in the office and involves building and managing a data platform using Databricks across regions.

You will design data pipelines, ensure data quality, and collaborate with stakeholders while documenting architectures and workflows to support governance and knowledge sharing.

Qualifications

• Bachelor's degree in computer science, data science, information technology, or related field.
• Proficiency in Python and SQL, with data modeling and database design experience.
• 5+ years as a Data Engineer with strong ETL/ELT understanding.
• Experience with API integration and data ingestion from multiple sources.
• Experience with data testing (functional and automated) including documentation and automation principles.

Responsibilities

• Collaborate with stakeholders to translate data needs into technical specs.
• Design, develop, and run data pipelines and ETL/ELT processes with emphasis on testing.
• Implement data quality checks to ensure data accuracy and integrity.
• Coordinate with cross-functional teams on goals, timelines and deliverables.
• Monitor data workflows for performance and reliability; implement improvements.
• Create and maintain documentation for data processes, architectures, and workflows.

Skills

Python

SQL

ETL/ELT

Data modeling

APIs

Data testing

Documentation

SCRUM

Education

Bachelor's degree in computer science / data science / IT or related field

Tools

Databricks

Apache Spark

Snowflake

AWS

Azure

Job description

Marsh in Louisville, KY is seeking a Data Engineer to join the Applications Development team. This hybrid role requires working at least three days a week in the office and involves building and managing a data platform using Databricks across regions.

You will design data pipelines, ensure data quality, and collaborate with stakeholders while documenting architectures and workflows to support governance and knowledge sharing.