Data Warehouse Engineer

About Lantern

Lantern is the specialty care platform connecting people with the best care when they need it most. By curating a Network of Excellence comprised of the nation's top specialists for surgery, cancer care, infusions and more, Lantern delivers excellent care with significant cost savings to employers and their workforces. Lantern also pairs members with a dedicated care team, including Care Advocates and nurses, for the entirety of their care journey, helping them get back to good health, back to their families and back to work. With convenient access to specialists nationwide, Lantern means quality care is within driving distance for most. Lantern is trusted by the nation's largest employers to deliver care to more than 6 million members across the country. Learn more about us at lanterncare.com. 

Lantern is looking for an experienced, Senior Data Warehouse Engineer to join our growing Analytics Engineering team. This role will focus on building and maintaining robust ELT pipelines, designing scalable data models, and optimizing analytic performance within the Lantern Analytic Platform. The ideal candidate will have hands-on experience with Azure Data Factory, Fivetran, and dbt, and a strong understanding of modern data architecture and performance tuning.

Location: Hybrid - at least 3 days/week in our Dallas, TX offices
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Responsibilities:**

• Implement and Monitor ELT Pipelines: Design, develop, and maintain scalable ELT workflows using Azure Data Factory, Fivetran, and dbt. Proactively monitor pipeline health and troubleshoot issues to ensure reliable data delivery.
• Data Modeling and Collaboration: Partner with data analysts to understand business needs and translate them into efficient, well-documented data models using dbt. Continuously refine models to support evolving analytic requirements.
• Analytics Platform Optimization: Manage and optimize the Lantern Analytic Platform by monitoring query performance, implementing data partitioning strategies, and refining data models to enhance speed and efficiency.

Requirements:

• Bachelor's degree in Computer Science or a related field (or equivalent practical experience)
• A minimum of 5 years of professional experience as a Data Engineer, Data Warehouse Engineer, or similar role building production data pipelines
• A minimum of 5 years of production-grade experience with Python and SQL
• A minimum of 2 years of hands-on experience with Databricks (Spark/PySpark, Delta Lake, Workflows)
• Hands-on experience building ELT/ETL pipelines with tools such as Azure Data Factory, Fivetran, or dbt