Staff Analytics Engineer — Data Warehouse

Together AI•San Francisco, CA

1d•$240,000 - $275,000

About The Position

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.

Requirements

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.

Nice To Haves

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.
Python for data tooling: automation, data quality frameworks, custom dbt macros or operators.
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.

Responsibilities

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 DbtTaskGroup 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 proactively. You treat downstream consumers as customers.
Write clear documentation — model descriptions, column-level lineage, and business context — so analysts can self-serve with confidence.
Run or contribute to data reviews, data-driven planning discussions, and architecture reviews involving the warehouse.