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Description

This is a ground-up, greenfield build — Finance for one of Amazon Ads' newest bets in the agentic space. No legacy pipelines, no inherited dashboards, no pattern to follow. If you're energized by shaping data infrastructure from zero to one inside a fast-moving org, keep reading.

What we're building:

- A finance data platform powering the FAIM org (Full-Funnel Agentic Intelligence & Models) — the team building the next generation of agentic AI advertising products
- Pipelines and models that turn raw data into decisions for greenfield products
- Self-service reporting that scales spanning Engineering, Science, PM-T, and Design across multiple AI native advertising products

This is a startup team within Amazon Ads Finance with an ambitious vision and the runway to build it right the first time.

We're looking for a senior Data Engineer who brings:

- Deep SQL fluency and 5+ years architecting and operating production ETL on Redshift, Andes, or equivalent at scale
- Hands-on depth with the Amazon data stack — Datanet/ETLM, Cradle, Andes 3.0, Redshift Spectrum, EDX, and QuickSight (SPICE)
- Strong dimensional data modeling judgment — fact/dim design, SCDs, and the experience to make the right denormalization, partitioning, and lifecycle calls without supervision
- Python (or equivalent) for orchestration, data quality automation, and pipeline tooling beyond SQL
- A willingness to set the bar — define data quality, lineage, SLA, and reliability standards for the org and hold the line on them
- The ability to operate in ambiguity — turn open-ended finance and program questions into durable data products with minimal scoping help
- Excitement about leading the data partnership with Finance Managers, PM-Ts, Scientists, and Engineering, and mentoring more junior engineers as the team grows
- AI-native experience for automation and defect/opportunity identification using tools such as Kiro, Claude Code, or equivalent


Key job responsibilities
- Own it end-to-end — set the technical direction for the FAIM data warehouse, ETL pipelines, and reporting layer
- Build the tools — architect and operate Datanet/ETLM jobs, Cradle profiles, Andes datasets, and dashboards that finance partners trust as source of truth
- Land the data — integrate telemetry from across Amazon's data ecosystem (Andes subscriptions, EDX, S3, internal services) into a clean, query-ready layer
- Move fast — deliver on OP1/OP2 cycles, MBR/QBR rhythms, and ad-hoc executive asks with bias for action
- Simplify complexity — turn messy, multi-source data into well-documented dimensional models that scale with the org
- Raise the bar — drive code and design reviews and set data quality and pipeline reliability standards