Finance Analytics Engineer

Together AI•San Francisco, CA

About The Position

This is the first dedicated data hire on Together AI's Finance team. You will own the data layer that Finance runs on — building from scratch the models, pipelines, and reporting infrastructure that allow Strategic Finance, FP&A, and Accounting teams to get reliable answers quickly. The person in this role will have direct exposure to every part of the Finance organization and a real opportunity to shape how data-driven decision-making develops here as the company scales. A significant portion of your work will touch the data behind the economics of Together's infrastructure, which sits behind nearly every financial question we ask. You will work closely with Together's Data and Commerce engineering team, which owns the underlying billing pipelines and data warehouse. Your job is to define and build the modeling and reporting layer that turns raw operational data into finance-grade datasets — aligning on data contracts, representing Finance's requirements in data design decisions, and ensuring the metrics Finance depends on are correct, documented, and trusted.

Requirements

5+ years in an analytics engineering or data engineering role, with meaningful time supporting Finance or business operations teams
Expert SQL and production-grade dbt experience — models, tests, docs, snapshots, incremental strategies; you've designed a dbt project from scratch and maintained it
Hands-on experience with Snowflake and Airflow in production; familiarity with Astronomer and the Cosmos dbt integration is a strong plus
Solid dimensional modeling fundamentals: star schemas, slowly changing dimensions, fact table grain — you can apply Kimball principles without needing to look them up
Strong dashboarding skills with a track record of executive-facing reporting that people actually use; experience with Hex or a comparable notebook/BI tool
Clear communicator who can align stakeholders on metric definitions and translate between engineering tradeoffs and Finance requirements in both directions
High comfort with ambiguity; this role will require scoping problems from scratch, not executing against pre-defined specs

Nice To Haves

Experience with financial data or billing data such as ARR, usage-based billing, invoice reconciliation, revenue recognition patterns, etc.. We operate a usage-based 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.

Responsibilities

Own and evolve the dbt transformation layer for Finance: design, build, test, document, and maintain models covering billing, financial performance, compute unit economics, and operational metrics
Author and maintain Airflow DAGs (Astronomer-managed) that orchestrate Finance dbt runs, data quality checks, and downstream dependencies reliably
Deliver dashboards and reporting in Hex for the executive team covering financial performance, utilization, and key operating metrics
Partner with Strategic Finance, FP&A, and Accounting teams on the data infrastructure behind forecasting, cost modeling, and other financial analyses
Set data quality standards across Finance data products and own incident response when Finance-critical pipelines break
Build the data foundation – clean, well-structured, documented, and reliably maintained – that enables Finance team to self-serve data analysis and supports AI automation and agentic workflows