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Rogo

Data Engineer: Analytics

Rogo is hiring a Data Engineer: Analytics in New York, United States. It pays $150k-$230k a year and Level rates it ; you can apply on Rogo.

Level's read on this job

  • Level rates this data engineer role Works on AI ●●●○: the daily work is on AI products, without building the model.
  • The listed pay starts close to the typical data engineer posting on Level.
  • Tools and skills the posting cites include Snowflake, dbt and etl.
  • Level classifies it as a mid-level role.
  • Level lists 100 other open roles at Rogo.

AI in this role

Own analytics infrastructure, pipelines, and evaluation layers for agentic analytics surfaces at an AI-native financial tech company.

Snowflakedbt
etldata-pipelinesanalyticsevaluation

Why Rogo

Our mission is to transform global finance by empowering professionals at the world's top investment banks, private equity funds, and investment firms with AI that delivers unparalleled speed, accuracy, and insight. We're not just improving financial workflows; we're redefining them.

This is a unique opportunity to join a generational company driving transformation in one of the most important industries in the world. With a rapidly growing, global client base, proven product-market fit, and backing from world-class investors, we are scaling quickly and defining a new category of enterprise AI.

Our team is sharp, motivated, and deeply committed to Rogo’s mission. We take ownership of complex problems and stay relentlessly focused on our users. If you thrive in a fast-paced environment, demand excellence, and want to help build the future of finance, we invite you to join us.

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.

Who You Are

  • You thrive in fast-paced environments. You are high-intensity and care a lot about what you do, and you're ecstatic to work at a startup.

  • You are ambitious. You have fun solving problems that others think are impossible.

  • You are curious. You find joy in learning about AI, technology, and finance.

  • You are an owner. You are autonomous, self-directed, and comfortable working with ambiguity.

  • You are collaborative, organized, thoughtful, and kind.

Why Join Rogo?

  • Up and to the right: Rogo has strong product adoption with the world's leading financial institutions, and we are still early. The upside is enormous.

  • Extraordinary team: we take talent density seriously. You'll do the best work of your career alongside some of the sharpest people in AI and finance.

  • A one-of-one problem: bringing AI to the core of how Wall Street works is one of the most ambitious, technically demanding, and consequential problems today. There is nowhere else you can work on it at this scale.

  • Real ownership: You'll own real surface area and watch the world's most sophisticated users rely on your work.

  • Always at the frontier: we work at the edge of what the best models can do and turn it into products people trust. If you're obsessed with AI, this is where it's happening.

How we rate this

Data Engineer: Analytics at Rogo rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ETLData PipelinesAnalyticsEvaluationSnowflakedbt

Also common in data engineer jobs at this level

Pay

This job starts at
$150k
Typical data engineer job
$147k

This posting starts close to the typical data engineer job. See data engineer jobs

Questions you could be asked

  1. Tell me about a project where etl was part of your work. What did you do?
  2. Tell me about a project where data pipelines was part of your work. What did you do?
  3. Tell me about a project where analytics was part of your work. What did you do?
  4. Tell me about a project where evaluation was part of your work. What did you do?
  5. Walk me through how you've used Snowflake in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: ETL, Data Pipelines, Analytics, Evaluation, and Snowflake. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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