Staff Data Warehouse Engineer

Together AI•San Francisco, CA

12h•$240,000 - $275,000

About The Position

Together AI is building high-performance inference compute and the software platform around it. We’re looking for a Staff Data Warehouse Engineer with strong fundamentals and high growth potential to evolve into a technical lead. You’ll design and operate the warehouse from bronze → silver → gold , own core data models and metrics, and raise the bar on data quality and governance across the org.

Requirements

Strong warehouse fundamentals and production experience delivering trusted datasets and metrics.
Expert SQL (window functions, dimensional modeling, performance tuning).
Hands-on with dbt (models, tests, docs, snapshots, macros) and Airflow (DAG design, backfills, reliability).
Solid Python for data tooling and automation; experience with Spark (PySpark/SQL) is a plus.
Practical experience with SCD2 , star schemas, and handling slowly changing business entities.
Strong stakeholder management: you can drive alignment on definitions, tradeoffs, and delivery timelines.
High standards for data quality, reliability, and maintainability.

Responsibilities

Architect and operate 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).
Design analytics-ready models: SCD Type 2 , star schemas , and appropriate normalization for upstream canonical layers.
Lead Master Data Management (MDM) patterns (golden records, reference data, deduping, identity resolution).
Implement and automate data quality checks (freshness, nulls, referential integrity, distribution drift, anomaly detection).
Establish data governance habits: data stewardship , ownership, SLAs, and clear definitions for “source of truth.”
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.
Mentor engineers and contribute to standards (code review, design docs, runbooks), paving the path to tech lead.