# Data Engineer: Analytics at Rogo

- Company: Rogo
- What the company does: Rogo is the trusted AI partner to the world’s leading financial institutions. Rogo helps finance teams make better decisions and build smarter teams. Backed by Kleiner Perkins and Sequoia.
- Company website: https://rogo.ai/
- Type: Startups (AI role)
- Level: Mid level
- Location: New York City
- Work setup: On-site
- Pay: $150K to $230K base salary per year (USD)
- Posted: 2026-06-25
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/rogo/f78497ad-e879-4b79-9a28-09a9cdcf94be
- Page: https://www.1752.vc/careers/jobs/rogo-data-engineer-analytics/

## About the role

Analytics at Rogo defines the metrics, runs the pipelines behind them, and turns both into decisions and a better product. We’re hiring a Data Engineer to take full ownership of our Analytics infrastructure and build it into something that scales confidently with the company. This is a hands-on, high-leverage role: pipelines, orchestration, and reliability. If you are an experienced data engineer excited about how your role can evolve at an AI-native organization, this is the opportunity for you.

## What they're looking for

- 5+ years of professional experience in data engineering, analytics engineering, or a closely related role, with a track record of owning production data systems
- Deep SQL proficiency and fluency in a modern cloud data warehouse (Snowflake preferred)
- Hands-on dbt experience: models, tests, macros, incremental strategies, and a real point of view on what makes a well-structured transformation layer
- Strong Python for pipeline and transformation work, and comfort with orchestration and modern ETL tooling
- You have a real point of view on keeping a fast refresh cadence as event volume geometrically increases with our business: partitioning strategies, incremental model design, storage tiering, etc
- Genuine fluency with AI products. You’ve used agentic tools seriously and have a working picture of what happens between a question and an answer: retrieval, tool calls, model turns, and where cost accrues

Tags: Engineering