Data Engineer - Analytics & AI

Phaxis; Nashville (TN); On-site; USD 90,000 - 130,000; Full time

Job summary

Phaxis in Nashville, TN is seeking a data engineer to develop and maintain data pipelines, models, and reporting solutions using Python and SQL to support business and analytics needs.

You will build Snowflake ETL pipelines for ML/AI initiatives, cleanse and analyze large datasets, create dashboards and lightweight data applications, and collaborate with business and technical teams to drive reliable data solutions that enable informed decision-making.

Responsibilities

• Develop and maintain data pipelines, data models, and reporting solutions using Python and SQL to support business and analytics needs.
• Build and automate ETL pipelines in Snowflake to prepare and transform data for ML/AI initiatives.
• Clean, validate, and analyze large datasets to ensure data quality and identify trends that support business decision-making.
• Create dashboards, reports, and lightweight data applications to provide stakeholders with actionable insights.
• Perform SQL query tuning, data pipeline debugging, and performance optimization to improve reliability and efficiency.
• Partner with business and technical teams to gather requirements, translate business needs into data solutions, and support implementation and ongoing enhancements.
• Contribute to data governance, documentation, platform improvements, and adoption of emerging data, analytics, and AI technologies.

Job description

• Develop and maintain data pipelines, data models, and reporting solutions using Python and SQL to support business and analytics needs.
• Build and automate ETL pipelines in Snowflake to prepare and transform data for ML/AI initiatives.
• Clean, validate, and analyze large datasets to ensure data quality and identify trends that support business decision-making.
• Create dashboards, reports, and lightweight data applications to provide stakeholders with actionable insights.
• Perform SQL query tuning, data pipeline debugging, and performance optimization to improve reliability and efficiency.
• Partner with business and technical teams to gather requirements, translate business needs into data solutions, and support implementation and ongoing enhancements.
• Contribute to data governance, documentation, platform improvements, and adoption of emerging data, analytics, and AI technologies.