Analytics Engineer; Data Warehouse

Staff Analytics Engineer — Data Warehouse
About the Role

Together AI is building high-performance AI inference infrastructure and the software platform around it. We’re looking for a senior Analytics Engineer who sits at the intersection of data engineering and business intelligence — someone who can turn raw, complex data into clean, trusted, well-documented models that the whole company can reason from.

You’ll own the transformation layer of our data warehouse: shaping bronze/silver/gold models, designing dimensional schemas, and acting as the connective tissue between engineering systems and business stakeholders. You are equally comfortable deep in a dbt project and in a room with Finance, GTM, and Product aligning on definitions.

• Expert SQL
  : window functions, complex aggregations, query optimization, cost‑aware pattern selection, proficiency in Snowflake or equivalent cloud warehouse.
• dbt
  : deep, production‑grade experience — models, tests (singular + generic), docs, snapshots, macros, packages, and incremental strategies. You’ve designed a dbt project from scratch and maintained it in production.
• Airflow / Astronomer
  : production DAG authoring, backfill handling, reliability patterns, and the Cosmos dbt integration.
• Dimensional modeling
  : you’ve read Kimball (or absorbed the equivalent), know the difference between star and snowflake schemas by feel, understand slowly changing dimensions, and can explain why a fact table’s grain matters.
• Stakeholder management
  : demonstrated experience partnering with non‑technical stakeholders, driving metric alignment, and delivering trusted data products — not just pipelines.
• Strong written communication: your documentation and async updates are clear enough that people don’t need to ask follow‑up questions.
• Experience with financial data or billing data — ARR, usage‑based billing, invoice reconciliation, revenue recognition patterns. We operate a usage‑based inference billing system and this context transfers directly.
• Experience with PII handling, data masking, access‑tier modeling, or compliance work (SOC 2, ISO 27001, GDPR, CCPA).
• Familiarity with lakehouse patterns (Iceberg, Delta, Hudi) and hybrid warehouse/lake architectures.
• Experience with Hex, Metabase, or similar notebook/BI tooling that sits on top of your dbt models.
• Prior experience in a high‑growth AI/ML infrastructure or platform company.

• Own and evolve the dbt transformation layer: design, implement, test, document, and maintain modular dbt projects that cover billing, product usage, financial data, and operational metrics.
• Build analytics‑ready dimensional models following Kimball methodology: star schemas, conformed dimensions, fact tables with the right grain, and SCD Type 2 for slowly changing entities.
• Design for correctness, performance, and cost — partition strategies, incremental models, and avoiding full‑table scans.
• Build and maintain a semantic/metrics layer with consistent, auditable metric definitions reused across notebooks, BI, and APIs.
• Author and maintain Airflow DAGs (Astronomer‑managed) that orchestrate dbt runs, data quality checks, and downstream dependencies reliably.
• Apply solid DAG design: idempotent tasks, proper backfill strategies, SLA alerting, and clean dependency graphs.
• Work in our Cosmos (dbt + Airflow) integration — you know when to use a Dbt Task Group  vs a custom operator.
• Implement data quality checks at every layer: freshness, null/uniqueness tests, referential integrity, distribution drift, and business‑rule assertions.
• Drive data stewardship practices: ownership, SLAs, clear “source of truth” definitions, and change communication.
• Handle PII fields correctly — masking, anonymization, and access‑tier alignment. Compliance experience (SOC 2, ISO 27001, or similar) is a meaningful plus.
• Be the analytical partner to Finance, GTM, Product, and Engineering — translate business questions into durable data models, not one‑off queries.
• Drive alignment on metric definitions, data ownership, and delivery tradeoffs across stakeholders with competing priorities.
• Communicate data quality issues, model changes, and breaking changes…