Rogo
Data Engineer: Analytics
New York City. Posted 4 months ago
The role
The Data Engineer will own the analytics infrastructure including ETL pipelines, Snowflake, dbt models, and orchestration. You will design evaluation and testing layers for agentic analytics surfaces and manage core KPIs. This role involves turning internal analytics work into customer-facing product features.
What you will do
- Own analytics infrastructure including ETL pipelines, Snowflake, dbt models, and orchestration.
- Design and run the evaluation and testing layer for agentic analytics surfaces.
- Own core KPI definitions, pipelines, and documentation.
- Develop deep product understanding.
- Partner with engineering and product to turn internal work into customer-facing features.
- Take ownership of a specific domain of analytics work.
Required qualifications
- 5+ years of professional experience in data engineering, analytics engineering, or a closely related role.
- Track record of owning production data systems.
- Deep SQL proficiency.
- Fluency in a modern cloud data warehouse.
- Hands-on dbt experience with models, tests, macros, and incremental strategies.
- Strong Python for pipeline and transformation work.
- Comfort with orchestration and modern ETL tooling.
- Knowledge of partitioning strategies, incremental model design, and storage tiering.
- Fluency with AI products and understanding of retrieval, tool calls, and model turns.
- Comfort operating with autonomy across multiple stakeholders and domains.
Nice to have
- Growth-stage experience or familiarity with high-scale data.
- Experience building evals or test harnesses for LLM-powered systems.
- Experience with single- and multi-tenant customer architectures.
- Familiarity with Salesforce, Intercom, or usage-based billing data models.
- Experience with third-party financial or B2B data vendors.
- Background in financial services, enterprise SaaS, or vertical AI.
- Snowflake proficiency
About the company
Rogo aims to transform global finance by providing AI to investment banks, private equity funds, and investment firms. The company is scaling quickly and defining a new category of enterprise AI.