Data Engineer (Remote)

The Phia Group is a service-oriented organization assisting employee health plans nationwide. We provide innovative cost-cutting solutions and expanding service offerings. Our success is driven by our talented and committed team, which includes being named one of USA Today’s Top Workplaces for 2026.

About the role

The Data Engineer supports the development, maintenance, and optimization of data pipelines and analytics-ready datasets. They collaborate across multiple teams and stakeholders to solve complex problems and support data-driven initiatives.

Responsibilities

• Build, maintain, and optimize data pipelines utilizing Azure Data Factory, ensuring data is ingested, transformed, and delivered to Snowflake reliably for analytics
• Implement monitoring, alerts, and testing of data pipeline performance, data quality metrics, and lineage to ensure trustworthy data delivery
• Troubleshoot data issues and perform root cause analysis to proactively resolve operational issues
• Document data structures, processes, architectural decisions, and best practices for knowledge sharing
• Develop, maintain, and optimize Snowflake objects (schemas, tables, views) and SQL transformations to produce curated, analytics-ready datasets
• Collaborate with analysts, stakeholders, and product owners to translate business needs into data requirements and stable technical implementations
• Enable data for AI/ML use cases by preparing feature-rich datasets, supporting feature engineering, and ensuring data consistency for model training and inference
• Support deployment and operationalization of machine learning models by integrating pipelines with ML workflows (e.g., batch/real-time scoring)
• Continually improve ongoing reporting and analytics, automating or simplifying self-service or manual processes
• Implement version control practices for all data engineering code and documentation

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related field; or equivalent experience
• 5+ years of experience in data engineering or business intelligence roles working with ETL, data modeling, data architecture, and developing pipelines and applications for analytics (e.g., BI, reporting, machine learning, deep learning)
• Solid programming skills in advanced SQL, Python, or other programming languages for data processing and automation
• Experience supporting or working with AI/ML workflows, including:
• Data preparation and feature engineering for machine learning models
• Integration of data pipelines with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch, or similar)
• Understanding of model lifecycle concepts (training, validation, deployment, monitoring)
• Expertise working with Snowflake for data warehousing, including experience with schema design, performance tuning, and optimization
• Proficiency with Git, Azure DevOps, and collaborative development best practices
• Experience designing, developing, and deploying end-to-end pipelines using Azure Data Factory

Qualifications

• Experience supporting or working with AI/ML workflows, including:
• Data preparation and feature engineering for machine learning models
• Integration of data pipelines with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch, or similar)
• Understanding of model lifecycle concepts (training, validation, deployment, monitoring)
• Expertise working with Snowflake for data warehousing, including experience with schema design, performance tuning, and optimization
• Proficiency with Git, Azure DevOps, and collaborative development best practices
• Experience designing, developing, and deploying end-to-end pipelines using Azure Data Factory

Skills

• Advanced SQL
• Python
• Git
• Azure DevOps
• Azure Data Factory
• Snowflake

Benefits

• Unparalleled benefits for employees
• Company named one of USA Today’s Top Workplaces for 2026
• Recognized as one of the Top Places to Work in 2026 by The Boston Globe and Louisville Business First

Pay

Compensation is commensurate with experience.

Schedule

Hours are flexible to accommodate your schedule.