Data Engineer - Analytics & AI

Phaxis; Nashville (TN); On-site; USD 90,000 - 140,000; Full time

Job summary

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

You will build and automate ETL pipelines in Snowflake, prep and transform data for ML/AI initiatives, and ensure data quality. You will create dashboards and lightweight data apps for stakeholders and collaborate with cross-functional teams.

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.