Analytics Engineer — Data Warehouse at Together AI in San Francisco

Summary: Together AI is a research-driven artificial intelligence company focused on building high-performance inference compute and the software platform around it. They are seeking an early-career Data Warehouse Engineer to contribute to designing and operating their data warehouse, ETL pipelines, and core data models while ensuring data quality and governance. Responsibilities: - Contribute to building and maintaining a medallion/curated data warehouse stack (bronze/silver/gold) for product, usage, billing, and operational data - Build and maintain Airflow orchestrated pipelines and dbt transformation projects (modular, tested, documented) - Help design analytics-ready models: SCD Type 2, star schemas, and appropriate normalization for upstream canonical layers - Learn and apply Master Data Management (MDM) patterns (golden records, reference data, deduping, identity resolution) - Implement data quality checks (freshness, nulls, referential integrity, distribution drift, anomaly detection) - Contribute to data governance habits: data stewardship, ownership, SLAs, and clear definitions for 'source of truth.' - Help build and maintain a business semantic layer (consistent metric definitions, dimensions, and reusable logic) used by notebooks/BI - Partner with stakeholders (Product, Engineering, Finance, GTM, Ops) to translate questions into durable datasets and metrics - Use SQL, Python, and Spark where scale demands it; optimize for correctness, performance, and cost Required Qualifications: - 0–4 years of professional experience (or strong internships/projects) working with data warehouses, pipelines, or analytics engineering - Solid SQL fundamentals — you're comfortable writing queries and have some exposure to window functions or dimensional modeling concepts - Some hands-on experience with dbt or Airflow, or strong eagerness to learn — coursework and personal projects count - Basic Python for scripting and data tooling; any exposure to Spark (PySpark/SQL) is a plus - Familiarity with data modeling concepts like SCD2 or star schemas — even if only from coursework - Good communication skills: you can ask clarifying questions, explain your reasoning, and work with stakeholders to understand their needs - High standards for data quality, reliability, and maintainability — you care about getting things right Required Skills: Data Warehousing, SQL, Window Functions, Dimensional Modeling, dbt, Apache Airflow, Python, Spark, Slowly Changing Dimensions Type 2 (SCD2), Star Schema Data Modeling, Data Quality Checks, Data Governance, Master Data Management (MDM) Benefits: Health insurance, Equity, Other competitive benefits