Forward Deployed Software Engineer

Job Description

Job Description

Job Overview

We’re hiring a Forward Deployed Engineer (FDE) to serve as the bridge between a modern data product and the messy reality of enterprise customer environments. This is a high-ownership, technical, client-embedded role where you’ll deploy the platform with top customers, architect integrations across modern data stacks, unblock adoption, and feed what you learn directly back into the core engineering team.

This is not traditional support and it’s not a lightweight solutions role—you’ll be expected to operate like a founding engineer inside the customer context, shipping real technical outcomes.

Location / Work Model

New York, NY (Hybrid)

Client-embedded delivery (on-site and/or virtual depending on engagement)

Responsibilities

• Embed directly with high-value clients to deploy the platform and drive adoption end-to-end.
• Run technical discovery and translate messy workflows into clear implementation blueprints.
• Architect and implement integrations across common enterprise data ecosystems (e.g., Snowflake, AWS, Databricks, and customer-specific tooling).
• Own execution for multi-week “data missions” with multiple stakeholders, dependencies, and constraints.
• Troubleshoot complex issues across data pipelines, permissions/security, reliability, latency, and data quality.
• Establish strong delivery hygiene: documentation, handoffs, repeatability, and clear technical communication.
• Provide a tight product feedback loop to core engineering: what breaks, what’s missing, what needs to be built next.

Requirements (Must-Haves)

• Strong customer-facing engineering background (forward deployed, solutions engineering, implementation engineering, or similar high-ownership delivery role).
• Proven ability to own technical outcomes inside complex enterprise environments.
• Hands-on experience working with modern data platforms and tooling (warehouse/lakehouse, pipelines, compute, cloud infrastructure).
• Strong debugging instincts and systems thinking—comfortable being “dropped in” and finding root causes quickly.
• Ability to design pragmatic architectures under real-world constraints (security, governance, access controls, cost, latency, messy upstream data).
• Excellent written and verbal communication with both technical and non-technical stakeholders.

Nice to Have

Deep exposure to any of: Snowflake, Databricks, AWS, Spark/EMR, streaming systems, governance/catalog tooling, or enterprise identity/security patterns.

Experience supporting ML, risk, fraud, or decisioning systems (or other high-stakes, low-latency domains).