Engineering Manager, Evidence Request Management

DataSnipper•New York, NY

9d•Hybrid

About The Position

DataSnipper is hiring an Engineering Manager in its New York office to lead the Evidence Request Management team for Alwin, its agentic automation platform for audit and finance. The team owns the evidence lifecycle, from generating requests from audit plans, risk assessments, and prior-year requests through document validation, client follow-up, and routing confirmed evidence into downstream workflows. The role is responsible for building agents that validate evidence against requests, audit standards, and firm requirements; communicate professionally with clients to resolve missing or incorrect documents; and determine when they can act autonomously versus when an auditor must be involved. The systems must support long-running workflows, changing deadlines, partial deliveries, re-requests, and engagement rollovers, with traceable actions and a strong experience for both audit firms and their clients. The Engineering Manager will set the team's technical direction and goals, hire and develop senior engineers, stay close to technical decisions, and partner with teams across Alwin, Product, Design, Agent Runtime, Document Intelligence, Evals, and audit experts. The role also focuses on measurable outcomes such as cycle time, chase volume, and first-time-right evidence, and on improving how engineering uses AI-assisted development. The position is hybrid, with at least three days onsite per week in DataSnipper’s New York City office.

Requirements

At least 3 years leading engineering teams, in addition to at least 5 years as a software engineer.
Empowerment-oriented leadership style and a track record of hiring and building high-performing teams.
Hands-on experience with agentic or LLM systems in production, including control loops, tool calling, human-in-the-loop workflows, and evaluation.
Experience building workflow-heavy products with multi-step state, notifications, and permissions; experience with external users is especially relevant.
Strong fundamentals in software architecture, distributed systems, APIs, and cloud-native development.
Strong product aptitude, including challenging problem framing, making roadmap trade-offs, and focusing on customer success.
Hands-on fluency with AI coding tools and agentic harnesses, and the ability to make a team measurably better through their use.
Comfort leading across time zones and offices, with clear written communication.

Nice To Haves

Experience with durable workflow engines such as Temporal or long-running, event-driven systems in production.
Document-processing experience, including OCR, classification, structured extraction, and quality metrics.
Conversational AI experience with external users, including tone, safety, and escalation.
Experience with case-management, ticketing, or client-portal products and email, file-sharing, or ERP integrations.
Practical agent evaluation experience, including datasets, offline and online experiments, and failure classification.
Experience in regulated domains such as audit, financial services, or healthcare, where traceability, approvals, and access control are required.
Experience with governance at scale, including permissions, approvals, and data access across enterprise customers and their clients.
Experience working closely with Forward Deployed Engineers, Solution Architects, and GTM teams to turn enterprise rollout learnings into platform capabilities.
Experience with Python, Azure, and observability tooling.
Domain experience in audit (PBC or engagement operations), accounting, or fintech.

Responsibilities

Lead the Evidence Request Management team through outcome-based goals.
Hire, grow, and retain senior engineers in New York and improve the team quarter over quarter.
Own the technical direction of Evidence Request Management end to end and make key technical decisions.
Build conversational agents that represent audit firms professionally, resolve client issues in plain language, and escalate questions when auditor input is needed.
Work with audit experts to translate evidence requirements into checks and measure how effectively agents identify deficiencies.
Define which agent actions can be autonomous, which require auditor approval, and how actions toward clients are traced.
Build resilient, long-running event-driven agents and workflows that handle partial deliveries, re-requests, deadline changes, and engagement-period rollovers.
Measure and improve cycle time, chase volume, first-time-right evidence, and the client experience.
Partner with Agent Runtime, Document Intelligence, Evals, Product, Design, and audit experts to deliver a coherent platform.
Set a high bar for AI-assisted engineering and help shape the use of AI in DataSnipper engineering.