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Rogo

Generative AI for financial research automation

GTM Systems Engineer

Sales OperationsConfirmed live in the last 24 hours
Full-timeNew York, NY, USA$170k - $200k /yrIn-person

About the Job

Requirements

  • At least 5 years owning go-to-market systems in a revenue, sales, or Revenue Operations organization, with most of that time hands-on administering Salesforce as a system of record.
  • Recent hands-on ownership of go-to-market tools beyond Salesforce, including integrations and access management between them.
  • Experience building and owning multiple components of full go-to-market technology stacks.
  • Hands-on experience with configure and troubleshoot configure-price-quote tooling, quote-to-cash, and customer lifecycle design, including quoting controls, contracts, entitlements, and renewals.
  • Recent, concrete experience applying artificial intelligence and large language models in go-to-market systems, demonstrated through one or two shipped examples, including how they were evaluated and what they changed.
  • Working familiarity with Apex, Salesforce Object Query Language, and Lightning Web Components sufficient to read, reason about, and validate code and use AI coding tools to build, test, and deploy it safely.
  • Comfort with SQL and sufficient Python or scripting skills to build and debug pipelines, integrations, and backfills.
  • Ability to scope an ambiguous business problem into a technical specification and execute it independently.
  • Strong written communication skills to explain system design and trade-offs to engineers and go-to-market leadership.

Responsibilities

  • Own Salesforce end to end, including its data model, schema, Flows, validation rules, permissions, record pages, DevOps hygiene, and platform reliability across the revenue lifecycle.
  • Build and maintain Salesforce customizations in Apex, SOQL, Flow, and Lightning Web Components, with testing, disciplined deployments, and durable documentation.
  • Administer and integrate the surrounding go-to-market technology stack, including Gong, Clay, Apollo, Lusha, Nooks, Default, LinkedIn Sales Navigator, DocuSign, and future tools.
  • Architect integrations between Salesforce and the rest of the stack, and own the API, OAuth, and access patterns needed for reliability.
  • Own the full user-access lifecycle across go-to-market systems, including provisioning, role and permission changes, and deprovisioning, with access designed by audience.
  • Evaluate, pilot, and consolidate tools, deciding which should be retained or retired.
  • Own configure-price-quote and quoting controls, including ramp and multi-year structures, approval paths, and Deal Desk guardrails.
  • Own the contract and entitlement layer, including order form and contract extraction, activated contract records, seat and license rollups, billing structure, and exception reporting.
  • Own renewal, expansion, and churn mechanics in the system, including generation, deduplication, forecasting, and reconciliation of renewal opportunities against contract cycles.
  • Design the in-system customer lifecycle so pipeline, deployment, health, and renewal use the same source of truth.
  • Design, build, and ship artificial-intelligence capabilities for the go-to-market process, including account health and scoring models, MEDDPICC scorers and deal inspection, account research agents, enrichment, routing, and call-signal extraction.
  • Build and extend Model Context Protocol servers and agent tooling so large language models can safely read and write go-to-market data, with appropriate field definitions, permissions, and API structure.
  • Establish evaluations, monitoring, confidence scoring, and guardrails so artificial-intelligence capabilities are observable, reversible, explainable, and measured against business outcomes.
  • Exercise judgment about where artificial intelligence helps or creates risk and where human review is needed.
  • Track developments in artificial intelligence and automation and apply useful tools to the go-to-market organization.
  • Maintain data quality as a production control through deduplication, segmentation, domain normalization, and explicit override fields with visible thresholds and reasons.
  • Design reporting at the point of data capture, including owners, reason codes, confidence tiers, evidence fields, and deduplication keys in the schema.
  • Conduct population-scale backfills using audit and load files segmented into ready, review, and skip categories, validated before production use.
  • Serve as the primary escalation point for go-to-market systems issues and address root causes in the system.

Desired Qualifications

  • Experience building or extending Model Context Protocol servers, agent frameworks, or large language model orchestration tooling.
  • Production experience with integration-platform-as-a-service or automation platforms such as Workato, n8n, Tray, or Zapier.
  • Experience with modern data-layer tools such as Snowflake, dbt, Fivetran, and Hightouch, or reverse ETL into go-to-market systems.
  • Salesforce Administrator, Advanced Administrator, or Platform Developer I certification.
  • Experience in a compliance-sensitive environment such as SOC 2, SOX, or IPO readiness.
  • Experience at a company that scaled significantly during the candidate's tenure.

About the Company

Rogo

Generative AI for financial research automation

Rogo builds a generative AI platform for financial institutions to automate research, workflows, and document creation by coordinating multiple AI models across proprietary and external content. Its model-agnostic orchestration lets users plug in different AI models and data sources to generate insights, reports, and outputs for analysts, traders, and portfolio teams. The platform serves investment banks, private equity, hedge funds, and asset managers, with deployments across 50,000+ professionals in 350+ firms, and it is expanding into wealth management and new regions in EMEA and APAC. Its goal is to help financial institutions scale research and documentation workloads and speed up decision-making by providing a flexible AI backbone that coordinates models, data feeds, and documents, differentiating through cross-model orchestration tailored to finance.

Company size
201-500
Company stage
Late Stage VC
Total funding
$345M
Headquarters
New York City, New York
Founded
2021

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