Full-Time

Staff Backend Engineer: Financial Data

Rogo

Rogo

51-200 employees

Bespoke AI research platform for finance

No salary listed

New York, NY, USA

In Person

Category
Software Engineering (1)
Required Skills
Rust
Redshift
Python
NoSQL
BigQuery
SQL
Java
Redis
Observability
REST APIs
C/C++
Snowflake
Google Cloud Platform

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Requirements
  • Bachelor's in Computer Science or related field.
  • 8+ years of professional software engineering experience, with a proven track record building complex, data-intensive backend systems.
  • 5+ years of hands-on experience with Google Cloud Platform or equivalent cloud stack, building and scaling production-grade services.
  • Deep expertise in Python for backend and API systems; proficiency in at least one strongly typed language (Rust, C++, or Java), with Java being a strong plus.
  • Mastery in designing large-scale distributed systems — asynchronous patterns, streaming, queuing, caching strategies, and robust observability (logging, metrics, tracing).
  • Strong command of relational databases and SQL — schema design, query optimization, indexing — alongside NoSQL, in-memory (Redis), and columnar stores (BigQuery, Snowflake, Redshift).
  • Proven experience building and operating large-scale data pipelines — both streaming and batch — integrating heterogeneous financial data sources with high reliability and throughput.
  • 3+ years of experience managing or working closely with globally distributed engineering teams.
  • Exceptional communication skills — able to articulate complex technical concepts to both technical and non-technical stakeholders, and to influence engineering direction across teams without direct authority.
  • Track record of shipping high-quality products in unstructured, fast-moving environments with full autonomy from conception to deployment.
  • Demonstrated passion for corporate finance, capital markets, and financial data — you understand the standards, workflows, and zero-tolerance for data quality issues that investment professionals demand.
Responsibilities
  • Own critical data infrastructure: Take complex requirements and turn them into robust, scalable solutions that ingest, transform, and serve financial datasets reliably — end to end, from conception to production.
  • Architect for scale and performance: Design and build highly scalable, cost-effective data processing pipelines that handle millions of documents and datapoints daily with very low latency and maximum throughput.
  • Drive data integration: Build systems that connect heterogeneous financial datasets — market data vendors, proprietary sources, third-party providers — into coherent, reliable, queryable platforms that power Rogo's AI layer.
  • Enable AI with great data: Work closely with the Core AI team to ensure financial data is structured, clean, and served in ways that maximize the performance and reliability of Rogo's models and retrieval systems. Critically, the data pipelines, transformations, and interfaces you build are themselves powered by the same AI techniques and innovation used across the rest of the platform — you are not just feeding the machine, you are part of it.
  • Handle customer data with care: Build secure, compliant integrations that treat enterprise customer data with the highest standards of trust, privacy, and reliability — from ingestion through to serving.
  • Raise the bar: Lead by example — write the kind of code, make the kind of decisions, and bring the kind of energy that naturally elevates everyone around you.
  • Be a cultural anchor: Foster a culture of learning, craft, and ownership not through direction, but through how you show up every day.

RogoData.com offers a secure generative AI platform built for elite financial institutions to accelerate research and decision-making. It combines financial-domain large language models with access to millions of internal and external documents, allowing users to search, analyze, and cite sources within a controlled environment. The platform is customized for each firm’s language and data, and is delivered through subscriptions with optional ongoing customization and support. Its goal is to save time, improve insights, and automate research workflows so teams can work faster and executives can find pivotal information quickly.

Company Size

51-200

Company Stage

Series D

Total Funding

$315M

Headquarters

New York City, New York

Founded

2021

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Simplify Jobs

Simplify's Take

What believers are saying

  • Rogo's AI agent Felix autonomously executes multi-step financial workflows like deal screening and CIM generation, replacing manual analyst steps.
  • Rogo expanded Manhattan HQ with 422 jobs and $160M Series D funding led by Kleiner Perkins, securing $6.5M NY tax credits and $14M R&D.
  • Rogo integrated with SS&C Intralinks DealCentre AI, enabling live diligence on deal content without manual data uploads or version control bottlenecks.

What critics are saying

  • Hebbia captures deal diligence with sentence-level citation accuracy, undermining Rogo's value for data-intensive due diligence at Rothschild and Lazard within 6-12 months.
  • F2 AI dominates buy-side private credit underwriting and PE monitoring with Excel-native AI, leaving Rogo's sell-side focus irrelevant for 35% of target clients within 9-15 months.
  • If client churn exceeds 15% due to competitor accuracy, Rogo faces existential cash flow failure within 18-24 months given its $160M Series D and 422-job expansion runway.

What makes Rogo unique

  • Rogo builds custom AI agents encoding proprietary workflows for model-agnostic execution across GPT 5.5, Claude Opus 4.7, and Gemini.
  • Rogo targets sell-side pitch and research automation, producing SOC2-compliant auditable Excel models and memos for investment banks.
  • Rogo founders Gabriel Stengel (ex-Lazard) and John Willett (ex-J.P. Morgan) use frontline banking experience to design agents for investment banking pain points.

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Benefits

Flexible Work Hours

Remote Work Options

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

2%

2 year growth

0%
Rogo
Jun 17th, 2026
Announcing Rogo’s Integration with SS&C Intralinks

Announcing Rogo’s integration with SS&C Intralinks Rogo now syncs directly with virtual data rooms in Intralinks DealCentre AI, so finance teams can run their full diligence with Rogo's intelligence applied on top of live deal content. Intralinks, an SS&C company, is the AI-enabled dealmaking platform for high-stakes transactions. Trusted by global investment banks, private equity firms, law firms, and corporations, Intralinks helps deal teams manage diligence, collaboration, and execution across mergers and acquisitions, capital raising, restructurings, and other strategic transactions. DealCentre AI is their AI-enabled dealmaking platform, purpose-built for the security, governance, and compliance that high-stakes M&A demands. With this integration, the deal content living in DealCentre is securely shared with Rogo, so teams can run diligence and analysis against a live, continuously current source of truth without ever leaving their working environment. What this unlocks is the removal of manual work between where deal documents live and where the actual analysis happens. Instead of downloading and re-uploading data room contents or chasing version control every time a file updates, teams select a deal and work directly from approved, governed content. This brings Intralinks' rigor into Rogo's data suite, built to be the single pane of glass for the 12 tabs financial professionals typically juggle. This integration will be available to Rogo customers with active deals on Intralinks DealCentre AI, as part of our growing ecosystem of financial data partners, surfacing alongside internal firm knowledge, public and private markets information, and other licensed sources within a single working environment.

Rogo
Jun 5th, 2026
Announcing Rogo’s Partnership with Daloopa

Rogo is pleased to announce a partnership with Daloopa, a fundamental data provider powering AI workflows in financial services, to bring Daloopa's data directly into the workflows that 35,000 finance professionals run on Rogo every day. What sets Daloopa apart is how they use AI for data extraction, have an accurate and complete fundamental dataset, and cover 5,500+ public companies globally, with each data point hyperlinked to its original source for transparency. Those sources include SEC filings, investor presentations, press releases, and more. This means financial professionals can pull metrics exactly as companies disclose them, with a breadth of company-specific KPIs that goes well beyond what traditional data vendors typically carry. This integration adds Daloopa's sourcing rigor to Rogo’s data suite - built to be the single pane of glass for the 12 tabs financial professionals typically juggle. "We are excited to partner with Rogo to bring Daloopa’s audit-ready financial data directly into the AI workflows of sophisticated financial institutions," said Gabriella Hernandez, Vice President of Partnerships at Daloopa. "Rogo has built a strong AI platform for research and analysis — used by thousands of financial professionals — and together we’re making it easier to access trusted data without leaving their working environment.” This integration will be available to Rogo customers as part of our growing ecosystem of financial data partners, surfacing alongside internal firm knowledge, public and private markets information, and other licensed sources within a single working environment.

Rogo
Apr 30th, 2026
Our $160M Series D and the Road Ahead | Rogo

Announcing $160M in new financing, led by Kleiner Perkins, with participation from Sequoia, Thrive Capital, Khosla Ventures, J.P. Morgan Growth Equity Partners, BoxGroup, Mantis VC, Jack Altman, Evantic and Positive Sum. 

Startup Fortune
Apr 29th, 2026
Rogo just turned junior banking grunt work into a $2 billion AI business.

Rogo just turned junior banking grunt work into a $2 billion AI business. Ai | Rogo, founded by former junior bankers, has reportedly reached a $2 billion valuation by automating the spreadsheet, research, and presentation work that dominates Wall Street analyst life, forcing banks to rethink the career ladder itself. Janet Harrison has over 16 years... April 29, 2026 Rogo has reportedly reached a $2 billion valuation by automating the spreadsheet, research, and presentation work that defines Wall Street's analyst pipeline, a reminder that vertical AI gets expensive fast when it attacks pain points firms already know how to pay for. There is a reason Wall Street keeps producing software targets that look obvious in hindsight. The industry runs on repetitive, expensive labor that smart people know is necessary but hate doing. Rogo has figured out how to package that resentment into a product. Founded in 2021 by former junior bankers, the startup has reportedly reached a $2 billion valuation by building AI tools that draft models, assemble research, and turn the first pass of a deal into something a human banker can actually use. That is not a generic productivity app. It is an attempt to rewire the most unpleasant layer of finance. The path to that valuation has been fast. Bloomberg previously reported in late 2025 that Sequoia was leading an investment valuing Rogo at about $750 million. In January, the company was already being described as a $750 million business after a $75 million Series C. Now Bloomberg says the number has doubled again to roughly $2 billion. That kind of jump only happens when investors believe the product has moved from novelty to necessity. And in finance, necessity is more valuable than admiration. If bankers can shave hours off comps analysis, pitchbook creation, and memo prep, the software does not need to be beautiful. It just needs to work. Rogo's advantage is not that it is trying to replace all of banking. It is more focused than that. It is going after the part of banking that everyone complains about but no one can escape, the endless cycle of data gathering, model building, and presentation formatting that junior analysts live inside. Those tasks are expensive because the firms doing them are already paying expensive people to do low-leverage work. The model here is simple. If a first-year banker can spend an entire weekend updating a deck, a machine that does the first 80 percent of that job creates immediate value. That is why vertical AI in finance is proving easier to monetize than horizontal AI tools. A broad assistant has to convince users it is useful across dozens of contexts. Rogo only has to convince bankers that it saves time, reduces errors, and speeds up deal execution. The pain point is already understood, the buyer is already wealthy, and the willingness to pay is unusually high. Nobody at a bulge-bracket bank needs to be educated on the cost of analyst turnover or the value of shaving hours off a comps book. They know the pain. They know the budget. That is the kind of market AI vendors dream about. It also helps that Rogo was founded by people who actually lived the workflow. Former junior bankers understand where the process breaks down because they spent their careers inside those broken processes. That matters more than it sounds like it should. In finance, credibility is a product feature. A team that knows the naming conventions, formatting expectations, and governance issues of a live model has a much easier time selling software to an industry that is allergic to sloppy output. The economics of analyst work. The deeper story is that Rogo is not just selling speed. It is attacking one of the most expensive apprenticeship systems in modern business. Entry-level banking is notoriously inefficient because firms use junior staff both to produce work and to train future leaders. That is a costly arrangement, but it has persisted because the output is billable and the training pipeline is seen as essential. If AI takes over the first draft, that balance starts to change. Firms may need fewer analysts, or they may need them for a different set of tasks. Either way, the old ladder gets shorter. That creates a management problem as much as a technology one. If the machine does the first pass, how do analysts learn the craft? If analysts spend less time building models from scratch, how do they develop judgment? If the most boring work disappears, what exactly becomes the rite of passage? These are not rhetorical questions. Banks use grunt work as a filtering mechanism. It is how they identify who can endure pressure, learn quickly, and handle detail without complaint. Software that eliminates that stage may improve margins, but it also risks weakening the pipeline that produces future dealmakers. That tension is why Rogo's rise matters beyond the product itself. It forces banks to decide whether training still has to be painful in order to be effective. In some cases, the answer will be no. In others, firms will realize they have outsourced too much of the learning process to software and need to rebuild it elsewhere. Either way, the analyst role will not look the same once AI is doing the first draft of the work. Why the valuation keeps rising. The market is rewarding Rogo because it sits in a category where ROI is easy to explain. If a bank can compress research time, reduce manual mistakes, and accelerate pitch preparation, the value proposition is obvious. The company has also been expanding aggressively. Its recent acquisition of Subset gave it a stronger spreadsheet agent that understands live financial models, formulas, and real banker workflows. That helps explain why the market is treating Rogo less like a point solution and more like an operating layer for finance. Rogo's own pitch is telling. It describes itself as purpose-built for finance professionals and emphasizes secure, enterprise-grade workflows. That is exactly the language you need to win in an industry where data quality, auditability, and confidentiality matter as much as speed. Unlike consumer AI, finance AI is not trying to win hearts. It is trying to cut minutes, lower error rates, and fit inside a workflow that already exists. That makes the adoption curve faster, because the software is not asking bankers to change what they do. It is asking them to do it with fewer keystrokes. The real reason this business can reach $2 billion is that Wall Street has already told the market what the pain is worth. Banks spend heavily on labor, and a lot of that labor is repetitive enough to automate. If Rogo can remove a meaningful share of that cost without sacrificing quality, the savings add up fast. Investors understand that. So do the banks. That is why a startup founded by former junior bankers now finds itself at the center of one of the clearest vertical AI categories in the market. The analyst role is being repriced. What happens next is less about whether AI can replace all banking work and more about where firms draw the line. Some tasks will remain too judgment-heavy, too relationship-driven, or too sensitive for full automation. Others will get swallowed quickly. But the analyst pipeline itself is already being repriced. The first draft belongs to software now. The question is whether the rest of the job still belongs to people in the same way it used to. That is the larger implication of Rogo's valuation jump. It is not just another AI milestone. It is evidence that finance, one of the most expensive and tradition-bound professional labor markets in the world, is already willing to pay for a machine that does the worst part of the job first. Once that happens, the old career ladder becomes a software workflow. And once a workflow is software, it is very hard to put the grunt work back where it used to be.

Rogo
Mar 16th, 2026
Scaling Rogo to Build the Future of Finance: Our $75M Series C and European Expansion | Rogo

We’ve raised a $75 million Series C led by Sequoia Capital to build the next generation of autonomous financial agents.