# Staff Analytics Engineer — Data Warehouse

**Company:** [Together AI](https://hotfix.jobs/companies/together-ai)
**Location:** San Francisco, CA
**Role:** Data Engineering
**Salary:** $240k – $275k/yr
**Skills:** SQL, dbt, Snowflake, Airflow, Astronomer, Dimensional Modeling, Python, Cosmos, Kimball, Star Schema
**Posted:** 2026-04-07

> Owns data warehouse transformation layer using dbt and Airflow, builds dimensional models for company-wide analytics, and partners with stakeholders to deliver trusted metrics on billing, usage, and operations.

## Job Description

## 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. 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**: read Kimball (or equivalent), difference between star and snowflake schemas, slowly changing dimensions, fact table grain.
- **Stakeholder management**: partnering with non-technical stakeholders, driving metric alignment, delivering trusted data products.
- **Strong written communication**: clear documentation and async updates.

**Strong plus**
- Experience with financial data or billing data — ARR, usage-based billing, invoice reconciliation, revenue recognition.
- Experience with PII handling, data masking, access-tier modeling, compliance (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.
- Prior experience in high-growth AI/ML infrastructure or platform company.

## Responsibilities

### Modeling & transformation
- Own and evolve the dbt transformation layer: design, implement, test, document, maintain modular dbt projects for billing, product usage, financial data, operational metrics.
- Build analytics-ready dimensional models following Kimball methodology: star schemas, conformed dimensions, fact tables with right grain, SCD Type 2.
- Design for correctness, performance, cost — partition strategies, incremental models, avoid full-table scans.
- Build and maintain semantic/metrics layer with consistent, auditable metric definitions.

### Pipeline orchestration
- Author and maintain Airflow DAGs (Astronomer-managed) for dbt runs, data quality checks, downstream dependencies.
- Solid DAG design: idempotent tasks, backfill strategies, SLA alerting, clean dependency graphs.
- Work in Cosmos (dbt + Airflow) integration — DbtTaskGroup vs custom operator.

### Data quality & governance
- Implement data quality checks: freshness, null/uniqueness tests, referential integrity, distribution drift, business-rule assertions.
- Drive data stewardship: ownership, SLAs, source of truth definitions, change communication.
- Handle PII fields: masking, anonymization, access-tier alignment.

### Stakeholder management
- Analytical partner to Finance, GTM, Product, Engineering — translate business questions into durable data models.
- Drive alignment on metric definitions, data ownership, delivery tradeoffs.
- Communicate data quality issues, model changes proactively.
- Write clear documentation — model descriptions, column-level lineage, business context.
- Run/contribute to data reviews, data-driven planning, architecture reviews.

## Compensation
US base salary range: $240,000 - $275,000 + equity + benefits.

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