Vouch Recruitment

Business Operations

• Partner with senior business leaders to identify, frame, and prioritize high-impact opportunities and operational problems
• Structure ambiguous questions into clear problem statements, hypotheses, and analytical plans
• Prepare clear, compelling materials for leadership discussion and recommendation forums, grounded in data and operational reality
• Support scenario modeling and operational planning in close partnership with Finance
• Pressure-test assumptions and surface tradeoffs to support high-quality decision making
• Perform ad-hoc analysis to validate projects and assumptions
• Help business leaders translate insights into clear decisions, including recommended paths forward
• Act as a neutral, trusted advisor who can synthesize perspectives across Brokerage, Sales, Placement, Ops, and Product
• Lead high-priority initiatives from concept through execution, either as: A senior individual contributor owning delivery, or A cross-functional project lead coordinating teams without formal authority
• Drive initiatives across Operating model changes, New product and segment expansion, Cross-functional process changes
• Coordinate work across multiple teams and roadmaps to reduce coordination overhead
• Establish clear goals, milestones, owners, and success metrics for initiatives
• Track progress, surface risks and dependencies, and course-correct as needed
• Define KPIs to measure the impact of decisions and initiatives
• Partner with Finance to connect outcomes to forecasts, models, and productivity assumptions
• Capture learnings and feed them back into future planning and decision cycles

Senior Business Operations Manager

Analytics Engineer

• Architect, build and optimize robust data models in dbt to power business reporting processes and analytics use cases
• Effectively speak with, and listen to, business stakeholders, engineers, designers, executives, etc., so that you can understand business context and support analysts/business stakeholders across GTM, Finance, and Product to design and build single-source-of-truth data assets to drive Vouch’s business
• Act as a liaison between Engineering and Analytics to define source and target fields for data assets
• Partner with upstream Engineering teams to develop new and adapt existing data models as our technology and business evolve
• Clearly document specs and data models with source, description, and field definitions to enhance collaboration, scalability, and usability
• Set up tests to ensure data quality, monitor daily job execution, and diagnose/fix issues to ensure SLAs are met
• Perform root cause analysis for data quality issues, and partner with upstream teams and business stakeholders to rectify dirty, incomplete, or unreliable data
• Maintain and enforce consistent data modelling standards, best practices, and design patterns
• Provide and receive constructive feedback through code reviews, technical specifications, and pair programming sessions
• Use software development best practices with a focus on version control, testing, reliability and maintainability
• Work in dbt, SQL, Snowflake, git, Airflow, Python, Stitch, Mode, and we welcome new ideas!

Insurance

• Establish trusted relationships with our customers, ensuring they are comfortable with and able to utilize the tools/services available through the Vouch platform
• Assess our clients’ risk profiles, advise them on appropriate risk management best practices as a licensed professional, and assist with the purchase of coverage
• Manage the client lifecycle from application submission through bind with a suite of carrier partners, including online carrier portals
• Construct and present customized insurance proposals to clients
• Plan and execute daily sales activities such as conducting video calls with clients, responding to emails, and maintaining appropriate documentation in our CRM
• Deliver consistent and timely responses, follow-through, and follow-up in response to client requests and issues
• Maintain a healthy pipeline and conduct pipeline reviews with your manager

Insurance Advisor

Full-stack Engineer

• Build and integrate data-powered platform services
• Design, build, and maintain backend services and APIs (primarily Python and Nest.js/Node.js) that integrate data directly into product workflows.
• Develop shared libraries, SDKs, and developer-facing tooling used across engineering teams.
• Implement event-driven systems and workflow orchestration using tools like Temporal and Dagster.
• Build retrieval-augmented and LLM-integrated workflows that enhance product experiences through summarization, classification, validation, or agentic automation.
• Partner with product engineering teams to embed data services seamlessly into user-facing products.
• Own data & platform integration across systems
• Integrate real-world transactional and operational data into software applications with reliability, quality, and performance in mind.
• Collaborate with data engineers to define and evolve data models, schemas, and interfaces that support platform and product needs.
• Improve upstream data quality and completeness by identifying issues, tracing root causes, and driving fixes.
• Build tools, guards, and checks to ensure data powering software workflows is accurate and trustworthy.
• Drive cross-functional engineering impact
• Work with product managers, engineering leaders, analysts, and domain experts to translate ambiguous requirements into clear, actionable software solutions.
• Fill product gaps by proposing workflows, APIs, or data-driven capabilities that aren’t explicitly defined but are necessary to deliver business value.
• Act as a technical leader within mixed pods, guiding architecture discussions, influencing system design, and unblocking teammates as needed.
• Participate in an on-call rotation for platform and data services.

Senior Software Engineer

Data Scientist

• Build and iterate on LLM & AI-powered product features
• Design, prototype, and ship features that use LLMs (e.g., content generation, summarization, classification, semantic search, assistants, recommendations).
• Work with engineers to integrate LLMs into the product via APIs or internal services (RAG, tools/functions calling, workflows, pipelines).
• Define evaluation strategies for LLM features (e.g., human-in-the-loop evaluation, rubrics, prompt experiments, offline/online metrics).
• Continuously refine prompts, data pipelines, and system design based on user behavior, quality metrics, and product goals.
• Own product analytics for data- & AI-powered features
• Partner with product managers and designers to define success metrics (e.g., adoption, engagement, conversion, retention, quality, time-to-value).
• Instrument new features: define events, ensure proper logging, and validate that data is correct and trustworthy.
• Analyze funnels, cohorts, user journeys, and experiment results to understand drivers of behavior and outcomes.
• Translate insights into clear recommendations that influence roadmaps, prioritization, and feature iteration.
• Work with real-world transactional data (SQL & NoSQL) Explore, clean, and transform data from transactional (OLTP), analytical (OLAP), and event-based systems.
• Work across SQL (e.g., Postgres, Snowflake) and NoSQL (e.g., Redis, document/Key-Value stores).
• Design data assets and features that are usable by both analytics workflows and LLM/ML systems.
• Define and track data quality metrics (completeness, consistency, timeliness, drift, schema changes).
• Build checks, monitors, and alerts to detect data issues that can affect analytics or AI/LLM performance.
• Work with data and engineering teams to diagnose root causes and drive changes that improve data quality over time.
• Use core ML concepts (feature design, model evaluation, bias/variance, generalization) to reason about LLM and non-LLM approaches.
• When appropriate, build and evaluate lighter-weight or traditional models (e.g., scoring, ranking, classification) to complement or replace LLM solutions.