Forward-Deployed Full-stack Engineer (React+Node)

This role is suitable for a Senior-level full-stack engineer with 4+ years of production experience using React, Node.js, JavaScript/TypeScript, Redux, and SQL. The candidate should be skilled in ETL/ELT, data integration, APIs, data warehouses, LLM or agent frameworks, and customer-facing technical communication. They should be familiar with integrating messy enterprise systems, building reliable metrics and pipelines, extending frontend and backend applications, configuring client workflows, and debugging data issues directly with end users.

How would you design a Node.js service that ingests data from a client’s ERP, CRM, and operational APIs?

I would define source-specific adapters behind a common ingestion interface, validate and normalize records, and make jobs idempotent with retries and checkpointing. I would also add structured logging, monitoring, rate-limit handling, and clear error reporting for data owners.

How would you turn messy client data into reliable business metrics using SQL?

I would first document the source fields and business definitions, then build staged transformations that clean, deduplicate, and reconcile the data before calculating metrics. I would validate results with row counts, null checks, referential integrity tests, and sample comparisons with the client’s reporting.

What techniques would you use to optimize a slow SQL query in a warehouse such as Snowflake?

I would inspect the query profile, reduce unnecessary columns and joins, filter early, and verify that joins use appropriate keys and cardinality. In Snowflake, I would also consider clustering, incremental transformations, warehouse sizing, and avoiding repeated computation through well-designed intermediate models.

How would you structure a React and Redux application for client-specific customizations?

I would keep shared product behavior separate from configuration-driven client behavior, using typed Redux slices and selectors for predictable state management. Reusable components, feature flags, and isolated integration boundaries would allow customization without creating a client-specific fork.

How would you safely extend an unfamiliar React and Node.js codebase during your first week?

I would map the application entry points, build and deployment process, data flow, test coverage, and key domain concepts before making a small end-to-end change. I would run the existing tests, reproduce a real user workflow, and ship an incremental change with clear validation and rollback options.

How would you diagnose a discrepancy between a client’s source report and a metric displayed in the product?

I would trace the metric from the frontend through the API, transformations, and raw source records, checking filters, time zones, joins, aggregation levels, and refresh timestamps. I would compare a small set of known records with the client’s data owner and add a regression test once the root cause is identified.

How would you build an LLM-powered agent that uses business data and tools reliably?

I would define explicit tools with typed inputs and outputs, constrain access to authorized data, and use prompts that clearly specify goals, context, and failure behavior. I would evaluate the agent with representative scenarios, measuring correctness, tool selection, groundedness, latency, and unsafe or unsupported responses.

How would you prevent an AI agent from exposing sensitive or unauthorized client data?

I would enforce authorization in the backend and tool layer rather than relying only on prompt instructions, applying tenant isolation, row-level permissions, and least-privilege credentials. Sensitive fields should be minimized or masked, while access, tool calls, and outputs should be audited according to the client’s security requirements.

How would you design an ETL or ELT pipeline to handle late-arriving and changing source data?

I would use incremental extraction based on reliable timestamps or change tracking, retain ingestion metadata, and support replaying a bounded time window. Upserts, deduplication keys, versioned transformations, and periodic backfills would keep downstream metrics correct when source records change after their initial arrival.

How would you explain a technical tradeoff, such as metric accuracy versus delivery speed, to a non-technical client?

I would describe the options in terms of business impact, assumptions, risks, and timelines rather than implementation details. I might recommend shipping a clearly labeled initial metric with known limitations, followed by a validation and refinement plan agreed upon with the client’s stakeholders.

Join a global management consulting firm as a Forward-Deployed Full-stack Engineer, helping deliver an AI platform that gives enterprise leaders a unified view of business performance. You’ll integrate warehouses, ERP, CRM, operational systems, and APIs; build reliable SQL-driven data pipelines; and customize React and Node.js features, agent workflows, and client metrics alongside consultants and end users. The stack includes JavaScript/TypeScript, React, Redux, Node.js, SQL, ETL/ELT, APIs, data warehouses, and LLM or agent frameworks; this is a 3–6 month, on-site engagement Monday–Thursday with remote Fridays in Waltham, Cincinnati, or Dallas. Salary is not specified; apply for the chance to ship quickly on a high-impact AI product while solving complex real-world data challenges directly with enterprise clients.