Engineering Manager, Evidence Request Management

DataSnipper•New York, NY

1d•$275,000 - $300,000•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 creating requests from audit plans, risk assessments, and prior-year requests through validating documents, communicating with clients, and routing confirmed evidence to downstream workflows. Its agents must operate over days or weeks, interact with people outside the firm, and determine when to act independently and when to seek human input, while keeping every action traceable and meeting the needs of large audit firms and their clients. The manager will lead through outcomes, build and develop a high-performing engineering team, and own the product’s technical direction while remaining close enough to the code to guide key decisions. The role includes developing client-facing agents that communicate professionally, encoding evidence standards with audit experts, deliberately setting autonomy and approval boundaries, and building resilient long-running workflows. Success includes improving evidence cycle time, reducing follow-up volume, increasing first-time-right evidence, and delivering a strong experience for both firms and their clients. The role works across Alwin with teams including Agent Runtime, Document Intelligence, Evals, Product, Design, and audit experts, many based in Amsterdam. The Engineering Manager will also help shape how DataSnipper engineering uses AI-assisted development. The position is hybrid, with at least three days onsite per week in the 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 approaches, and evaluation.
Experience building workflow-heavy products with multi-step state, notifications, and permissions; experience with users outside the customer’s organization is 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 experience making a team measurably better through their use.
Ability to lead across time zones and offices with clear written communication.

Nice To Haves

Experience with durable workflow engines such as Temporal or with long-running, event-driven systems in production.
Experience with document processing, including OCR, classification, structured extraction, and quality metrics.
Experience with conversational AI for external users, including tone, safety, and escalation.
Experience with case-management, ticketing, or client-portal products, and integrations with email, file-sharing, or ERP systems.
Practical experience with agent evaluation, 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 requirements.
Experience with governance at scale, including permissions, approvals, and data access across enterprise customers and their clients.
Experience working 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, including PBC or engagement operations, accounting, or fintech.

Responsibilities

Lead the team through outcomes by setting clear goals and trusting the team to determine how to achieve them.
Hire, grow, and retain senior engineers in New York, and improve the team measurably each quarter.
Own the end-to-end technical direction of Evidence Request Management and remain close enough to the code to make difficult technical decisions credibly.
Build conversational agents that represent the firm professionally, resolve client issues in plain language, and recognize when a question should be escalated to an auditor.
Work with audit experts to encode evidence standards for each request type and measure how effectively the agent identifies issues an experienced auditor would catch.
Define which agent actions can be taken autonomously, which require auditor approval, and how actions toward clients are made traceable.
Build resilient, long-running agent and workflow systems that handle partial deliveries, re-requests, deadline changes, and engagement-period rollovers.
Measure and improve cycle time, follow-up volume, first-time-right evidence, and the experience for both firms and clients.
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 shape how DataSnipper engineering uses AI.
Own the evidence lifecycle, including request generation and assignment, evidence validation, client communication, and routing confirmed evidence to downstream tasks and agent runs.
Deliver the client-facing portal, notifications, and permissions for external users.