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Home - Lessons - Forward Deployed Engineer Training - Module 3 — APIs and Data Modelling for Deployment - Mapping messy client data onto a clean model

Forward Deployed Engineer Training Mapping messy client data onto a clean model

Forward Deployed Engineer Training

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Module 1 — What a Forward Deployed Engineer Does

The FDE role: origin, where it sits between engineering and the customer

FDE vs software engineer vs solutions engineer vs success engineer

Where FDE roles are hired and what they pay

The deployment lifecycle a typical FDE owns

Setting up a realistic practice environment

Module 2 — Full-Stack Product Engineering Foundations

Frontend fundamentals for a deployable product UI

Backend services, routing and business logic

Authentication, sessions and role-based access

Repository structure and clean, reviewable code

Shipping a small full-stack feature end to end

Module 3 — APIs and Data Modelling for Deployment

Designing REST and GraphQL APIs a client can actually adopt

Schema design, migrations and versioning

Integration patterns: webhooks, queues and batch loads

Mapping messy client data onto a clean model

Contract testing and backwards compatibility

Module 4 — Cloud Deployment and CI/CD on AWS and GCP

Infrastructure basics: compute, storage, networking, IAM

Infrastructure as code with Terraform

CI/CD pipelines that deploy safely

Blue/green and canary releases, rollback strategy

Environment separation and secrets management

Module 5 — Containers, Runtime and Operations

Docker and container orchestration basics

Resource limits, scaling and health checks

Logging, metrics and tracing for a deployed system

On-call discipline and incident response

Keeping a client deployment running after handover

Module 6 — Data Pipelines and Integration

Ingesting client data from files, databases and APIs

Batch vs streaming and when each is appropriate

Data quality checks and reconciliation

Warehouses and analytics stores an FDE is expected to touch

Governance, PII and access on client data

Module 7 — Solution Design and Technical Discovery

Running a discovery conversation with a client

Writing a solution design document

Trade-off analysis: build vs configure vs integrate

Scoping an MVP and defending it

De-risking a deployment with prototypes

Module 8 — Customer-Facing Engineering and Communication

Explaining technical limits without jargon

Managing scope and expectations

Documentation a client team can maintain

Running a demo and a handover

Handling disagreement and escalation

Module 9 — Deploying AI and LLM Applications

Where LLM features fit in a deployed product

APIs, embeddings and retrieval patterns

Cost, latency and evaluation in production

Safety, guardrails and data handling

Shipping an AI-assisted feature end to end

Module 10 — Security, Compliance and Access in Deployments

Securing a client deployment

Access control and least privilege

Audit logs and compliance expectations

Secrets, key management and rotation

Reviewing a deployment before go-live

Module 11 — Capstone: Deploy a Production Solution End to End

Scoping a realistic client-style problem

Modelling data and building the application

Deploying to cloud with CI/CD and monitoring

Writing the solution design and handover docs

Demoing and defending the deployment

Module 12 — Interview Preparation and Portfolio

FDE interview loops and what they test

Presenting deployed projects as case studies

Resuming for FDE roles

Live coding and system design for deployment

Negotiating an FDE offer

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