Data Engineer: Scalable ETL & Analytics Pipelines

Compunnel, Inc.; Northern (KY); Hybrid; USD 85,000 - 125,000; Full time

Job summary

Compunnel, Inc. is seeking a Data Engineer to design, develop, and maintain scalable data pipelines that support enterprise analytics. You will leverage AWS, Snowflake, SQL, and Python to deliver robust ETL solutions and governance.

The role requires collaborating with cross-functional teams, translating business requirements into technical designs, and ensuring data quality through comprehensive testing and deployment in a hybrid environment.

Qualifications

• Bachelor's degree or equivalent experience in a data-related field.
• 5+ years in data analytics/engineering or related roles.
• Strong SQL and Python skills with ETL development experience.
• Experience with AWS services and Snowflake data platform.
• Knowledge of data governance and CI/CD practices.
• Excellent communication and cross-functional collaboration.

Responsibilities

• Design, develop, and maintain scalable ETL processes for data integration.
• Build and optimize databases for real-time and batch analytics.
• Integrate data from multiple sources to support BI initiatives.
• Troubleshoot pipelines to ensure data integrity and reliability.
• Translate business needs into technical data solutions.
• Develop SQL queries, data processing scripts, and reusable code.
• Collaborate with stakeholders and lead development from design to deployment.
• Perform testing/validation and support data governance initiatives.

Skills

SQL

Python

ETL development

AWS

Snowflake

Data governance

Analytical thinking

Communication

Education

Bachelor's degree or equivalent

Tools

AWS Glue

Redshift

Job description

Compunnel, Inc. is seeking a Data Engineer to design, develop, and maintain scalable data pipelines that support enterprise analytics. You will leverage AWS, Snowflake, SQL, and Python to deliver robust ETL solutions and governance.

The role requires collaborating with cross-functional teams, translating business requirements into technical designs, and ensuring data quality through comprehensive testing and deployment in a hybrid environment.