Data Warehouse Engineer

<h3><strong>About the Role</strong></h3> <p>Together AI is building high-performance inference compute and the software platform around it. We’re looking for an early-career Data Warehouse Engineer with strong fundamentals and high growth potential to grow into a technical lead over time. You’ll contribute to designing and operating our data warehouse, ETL pipelines and orchestration, work on core data models and metrics, and help raise the bar on data quality and governance across the org — with mentorship and support from experienced engineers.</p> <p>&nbsp;</p> <h3><strong>Requirements</strong></h3> <ul> <li>0–4 years of professional experience (or strong internships/projects) working with data warehouses, pipelines, or analytics engineering.</li> <li>Solid SQL fundamentals — you’re comfortable writing queries and have some exposure to window functions or dimensional modeling concepts.</li> <li>Some hands-on experience with dbt or Airflow, or strong eagerness to learn — coursework and personal projects count.</li> <li>Basic Python for scripting and data tooling; any exposure to Spark (PySpark/SQL) is a plus.</li> <li>Familiarity with data modeling concepts like SCD2 or star schemas — even if only from coursework.</li> <li>Good communication skills: you can ask clarifying questions, explain your reasoning, and work with stakeholders to understand their needs.</li> <li>High standards for data quality, reliability, and maintainability — you care about getting things right.</li> </ul> <p>&nbsp;</p> <h3><strong>Responsibilities</strong></h3> <ul> <li>Contribute to building and maintaining a medallion/curated data warehouse stack (bronze/silver/gold) for product, usage, billing, and operational data.</li> <li>Build and maintain Airflow orchestrated pipelines and dbt transformation projects (modular, tested, documented).</li> <li>Help design analytics-ready models: SCD Type 2, star schemas, and appropriate normalization for upstream canonical layers.</li> <li>Learn and