Data Engineer: Analytics

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.

In many ways this is also a product role. Rogo productizes how we use our own data — many of our Analytical features began as internal analytics work. You will be expected to know the product as well as any engineer here, because the systems you build both measure it and support its development. We expect everyone at Rogo to take ownership of making our product better for our users.

What You Will Own

• Own analytics infrastructure: the ETL pipelines, Snowflake, dbt models, orchestration, and the reliability of all of it.
• Analytics evals: design and run the evaluation and testing layer for our agentic analytics surfaces — internal agents that answer thousands of questions a quarter, and the customer-facing analytics our enterprise admins rely on. This is one of the defining problems of doing analytics at an AI company.
• Own core KPIs: the definitions, the pipelines that produce them, and the documentation that makes them legible. When a number moves, you can say whether the pipeline broke, the definition changed, or the business moved.
• Know the product inside and out: develop the same depth of product understanding as any engineer or salesperson at Rogo.
• Turn internal work into product: partner with engineering and product where our analytics infrastructure becomes a customer-facing feature.
• Pick up a lane of analytics work: as the foundation solidifies, take a domain of your own and go deep, so the team’s judgment grows alongside its infrastructure.

What You Will Need

• 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.
• Real curiosity about the business. You care about and take ownership of what a number means, not only that the job ran.
• Comfort operating with autonomy across multiple stakeholders and domains.

Bonus

• Growth-stage experience, or familiarity with the scale of data Rogo will be handling twelve months from now.
• 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 (Metronome or similar).
• Experience with third-party financial or B2B data vendors (LSEG, FactSet, Crunchbase, ZoomInfo, Apollo, or similar).
• Background in financial services, enterprise SaaS, or vertical AI.