Senior Data Warehouse Engineer

Lantern•Dallas, TX

5d•Hybrid

About The Position

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.

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
Strong understanding of data warehousing concepts, with hands-on experience designing, building, and optimizing data models (e.g., dimensional modeling)
Experience with query and pipeline performance tuning
Experience supporting analytics and reporting consumers, partnering with analysts and cross-functional teams
Hands-on experience using AI coding tools (e.g., Claude Code, Copilot) in day-to-day engineering work

Nice To Haves

Experience with Databricks Genie or Snowflake Cortex
Experience in the healthcare industry

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.