Senior Data Engineer

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

• W2 candidates only, we cannot consider C2C or 1099 for this opportunity.
• Candidates should be eligible to work for any employer in the United States without needing Visa sponsorship

Position Summary

We are seeking a highly skilled Data Engineer with 7+ years of experience designing, developing, and supporting enterprise-scale data platforms. This role will support a strategic initiative to migrate a legacy Netezza environment to Databricks, enabling next-generation analytics and data-driven decision making across customer-facing retail operations.

The successful candidate will play a critical role in modernizing a highly visible and business-critical customer data platform that supports operations. The ideal candidate brings deep expertise in Databricks, PySpark, data engineering best practices, and cloud-based data architecture, along with experience building scalable ingestion, transformation, testing, and monitoring solutions.

Key Responsibilities

• Provide support to the migration of legacy data assets from Netezza to Databricks.
• Design, develop, and optimize scalable data pipelines using PySpark.
• Build and support batch and streaming ingestion frameworks for enterprise data processing.
• Implement data transformation and conversion strategies to support platform modernization initiatives.
• Develop and maintain data solutions utilizing Databricks Lakehouse architecture and Medallion design patterns.

Establish and enforce engineering best practices, including:

• Coding standards
• Automated testing frameworks
• Monitoring and operational support procedures
• Collaborate with business stakeholders, architects, analysts, and engineering teams to deliver high-quality solutions.
• Perform code reviews and mentor junior team members on data engineering best practices.
• Troubleshoot and resolve performance, scalability, and data integrity issues.
• Drive continuous improvement initiatives across the data engineering ecosystem.
• Support integration testing, regression testing, and production deployment activities.

Required 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 implementing data quality frameworks, validation processes, and reconciliation strategies.
• Experience building and maintaining automated testing and integration suites.
• Experience creating and supporting CI/CD pipelines for data platforms.
• Strong understanding of cloud-based data architectures and modern analytics platforms.
• Excellent communication and collaboration skills.

Preferred Qualifications

• Experience with Netezza migrations or legacy Netezza environments.
• Experience with Delta Lake, Unity Catalog, and Databricks Workflows.
• Experience working with large-scale retail, customer, or omnichannel data platforms.
• Familiarity with Azure cloud services and modern data integration patterns.
• Experience with infrastructure-as-code and DevOps methodologies.
• Knowledge of data governance, lineage, and metadata management solutions.

Top Priority Skills:

Databricks, PySpark, Data Migration, Test Automation, CI/CD, Data Quality, Medallion Architecture, Streaming & Batch Processing