Engineering Manager, Agent Builder

DataSnipper•New York, NY

3d•$275,000 - $300,000•Hybrid

About The Position

DataSnipper is hiring an Engineering Manager to lead the Agent Builder team in New York. Agent Builder is part of Alwin, DataSnipper’s agentic automation platform for audit and finance. The team builds the capabilities firms use to turn their methodology and knowledge into agents and manage those agents throughout their lifecycle, from definition through retirement. Its knowledge and context layer brings together firms’ methodologies, guidance, templates, prior-year workpapers and engagement context so agents can work in ways grounded in each firm’s practices. The role owns the full agent lifecycle, including knowledge and context management; agent authoring and definition; testing and publication; execution and observability; and governance, versioning and retirement. The Engineering Manager will set technical direction, build and develop a high-performing team, and make decisions about agent-building abstractions and guardrails. They will make evaluation, traceability and auditability integral to the product, and collaborate with teams across Alwin as well as audit methodology experts, customer-facing teams and colleagues in other offices. The role is based in DataSnipper’s New York City office and follows a hybrid work model, with at least three days onsite per week.

Requirements

At least 3 years leading engineering teams, in addition to at least 5 years as a software engineer.
An empowering leadership style and a track record of hiring and building high-performing teams.
Hands-on experience with agentic or LLM systems in production, including context engineering and retrieval; understanding of control loops, tool calling, grounding and evaluation sufficient to challenge design decisions.
Experience building platform products with a lifecycle, including versioning, permissions, releases and deprecation for customer-built solutions.
Strong fundamentals in software architecture, distributed systems, APIs and cloud-native development.
Strong product aptitude, including challenging problem framing, making roadmap trade-offs and caring about customer success.
Hands-on fluency with AI coding tools and agentic harnesses, and experience making a team measurably more effective through their use.
Ability to lead across time zones and offices with clear written communication.

Nice To Haves

Knowledge management or enterprise search experience, including ingestion, chunking, embeddings, hybrid retrieval, document-level permissions and keeping content fresh.
Experience with knowledge graphs or structured representations of policies, procedures and rules.
Experience building low-code or configuration products, workflow designers, rule engines or domain-specific languages.
Practical agent evaluation experience, including datasets, offline and online experiments, failure classification and measuring the impact of context on quality.
Experience in regulated domains such as audit, financial services or healthcare, where traceability, approvals and access control are requirements.
Experience operating governance at scale, including permissions, approvals and change control across enterprise customers, teams and thousands of users.
Experience with Python, Azure and observability tooling.
Domain experience in audit methodology, accounting or fintech.

Responsibilities

Lead 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 over time.
Own the end-to-end technical direction of Agent Builder and stay sufficiently close to the code to make consequential technical decisions.
Build the context layer that grounds agents in firms’ methodologies, including how knowledge is captured, maintained, permissioned and retrieved, and how its impact on results is demonstrated.
Determine the right balance between builder flexibility and guardrails so agents can encode real methodology while remaining consistent and trustworthy.
Design lifecycle capabilities—including versioning, approvals, rollout and sunsetting—as core product features.
Make evaluation, observability and auditability integral to agents firms can rely on.
Partner with Agent Runtime, Evals, Document Intelligence, Product, Design and audit methodology experts to deliver a coherent platform.
Set the standard for AI-assisted engineering, using AI to tackle harder problems and shaping how DataSnipper engineering works with AI.
Work with Forward Deployed Engineers, Solution Architects and GTM teams to turn lessons from enterprise rollouts into platform capabilities rather than one-off solutions.