Senior Data Engineer – Databricks & PySpark Architect

Ascendum Solutions; Cincinnati (OH); On-site; USD 120,000 - 180,000; Full time

Job summary

Ascendum Solutions in Cincinnati, OH is seeking a Data Engineer with 7+ years of experience to lead the migration of a legacy Netezza environment to Databricks for retail analytics.

You will design scalable PySpark pipelines, build batch and streaming ingestion, and champion data quality, testing, and CI/CD practices across the data platform. Collaboration with stakeholders and mentors is essential.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• 7+ years of Data Engineering experience in enterprise environments.
• Strong hands-on experience with Databricks (required).
• Advanced proficiency in PySpark development.
• Experience developing both batch and streaming data pipelines.
• Strong SQL and data modeling expertise.
• Experience creating and supporting CI/CD pipelines for data platforms.

Responsibilities

• Migrate legacy data assets from Netezza to Databricks.
• Design, develop, and optimize scalable PySpark pipelines.
• Build batch and streaming ingestion frameworks for enterprise data.
• Implement data transformation and conversion strategies.
• Develop and maintain Databricks Lakehouse architecture and Medallion patterns.
• Mentor junior engineers and enforce data engineering best practices.
• Troubleshoot performance, scalability, and data integrity issues.

Skills

SQL

Data Modeling

CI/CD

Test Automation

Mentoring

Communication

Education

Bachelor's degree in CS/IS/Engineering

Tools

Databricks

PySpark

Delta Lake

Unity Catalog

Databricks Workflows

Azure Cloud

Job description

Ascendum Solutions in Cincinnati, OH is seeking a Data Engineer with 7+ years of experience to lead the migration of a legacy Netezza environment to Databricks for retail analytics.

You will design scalable PySpark pipelines, build batch and streaming ingestion, and champion data quality, testing, and CI/CD practices across the data platform. Collaboration with stakeholders and mentors is essential.