Hire Backend Developers remotely from our vetted global talent

A backend developer owns the server-side half of a product: the API that defines what the client can do, the business logic behind every state change, and the data layer that persists it all. The role exists because a full stack engineer's depth runs out somewhere, usually at the boundary where requests-per-second, schema design, and distributed-system trade-offs start to dominate the work. At Terminal, backend hires are the engineers product teams reach for when the hard problem lives on the server.

API design and ownership: The contract between client and server is the engineer's first responsibility.

• REST, GraphQL, and RPC choices that match the front end's actual access patterns

• Versioning strategy that lets the API evolve without breaking older clients

• Authentication, rate limiting, and the boundary that protects the database

• Documentation that survives the engineer who wrote it (OpenAPI, GraphQL introspection, well-named types)

Business logic and domain modeling: The part of the codebase that encodes how the product actually works.

• Translating product specs into validation rules, state machines, and authorization policies

• Domain modeling that resists feature creep without freezing the schema

• Service boundaries that match the team structure, not the framework defaults

• Long-tail edge cases that show up in production but not in design docs

Data layer ownership: The schema is downstream of every query, and the engineer designs it deliberately.

• Schema design that matches the actual read and write patterns

• Indexing decisions based on EXPLAIN output, not speculation

• Online migrations that ship against a live production database with zero downtime

• The choice between Postgres, MySQL, MongoDB, Redis, vector stores, and the trade-offs each implies

Infrastructure and operations: Senior backend engineers run their code in production.

• Containerization with Docker, deployment to Kubernetes, ECS, or platform-as-a-service

• CI/CD pipelines they configure, not just consume

• Logs, metrics, traces, and the ability to read them fluently when the page is on fire

Common stacks worth knowing: Real-world backend engineers usually go deep in one or two combinations.

• Node.js with Express, Fastify, or NestJS for JavaScript-only teams

• Python with FastAPI or Django for API-heavy products and ML adjacencies

• Go for high-throughput services and cloud infrastructure

• Java with Spring Boot for enterprise systems with strict typing requirements

• Ruby on Rails when the business logic moves faster than the framework choice

The case for a backend specialist is almost always a system-complexity argument. When a product crosses the threshold where a single full stack engineer cannot hold the whole server in their head, hiring a backend developer who lives in that complexity full-time becomes the highest-leverage move on the roadmap. The case against shows up when the slice still matters more than the depth.

Throughput and latency demand specialization: Anywhere the cost of milliseconds is real.

• Payment systems, real-time bidding, video streaming, large-scale messaging, search indexing

• Profiling and tuning at the database, framework, and runtime level

• Capacity planning that reads load curves rather than guessing

• Hot-path optimization that requires knowing what the runtime is actually doing

Data integrity is non-negotiable: When the consequences of a corrupted write are bigger than the engineering cost.

• Financial systems where eventually-consistent is unacceptable

• Audit trails, immutable event logs, and reproducible state

• Distributed transactions, sagas, and idempotency where the network is unreliable

• Recovery and rollback procedures that have been actually rehearsed

API surface area is the product: When the API is what the customer pays for.

• Public APIs that need backwards compatibility years after launch

• Internal APIs that other teams depend on without coordination

• GraphQL schemas that constrain what client teams can ask for

• SDK design that hides complexity without leaking abstraction

AI Fluency multiplier: Agentic AI workflows have changed how backend engineers ship code, and the gains compound on server-side work.

• An AI Enabled engineer running Cursor or Claude Code with human-in-the-loop review can refactor across services and tests in a single session

• An AI Native engineer orchestrates parallel agents to land migrations, API updates, and observability changes in the same pull request

• The productivity gap between AI-fluent backend engineers and unassisted specialists keeps widening

• Terminal classifies every engineer in AI Assisted, AI Enabled, or AI Native tiers and surfaces those signals at hire time

When not to hire a backend specialist: Generalists win on small teams shipping CRUD.

• Product teams of 2 to 5 people where coordination cost matters more than depth

• Internal tools where a full stack engineer can ship the whole feature faster

• Prototypes where the data model will change three times before production

• Hire a full stack engineer when the slice matters more than the depth

A senior backend developer's job description is broader than the job posting suggests, but the day-to-day is concrete. Here is what they actually own.

API delivery, end-to-end: The default unit of work.

• Design the endpoint or query, write the implementation, ship the migration, monitor the rollout

• Behind a feature flag in staging first, then ramp by percentage in production

• Own the change from kickoff to monitoring after deploy

• Pair with the front-end engineer on the contract before writing code, not after

Database modeling and migrations: The schema is the engineer's most consequential decision.

• Pick the shape that matches the actual access patterns the application will generate

• Index for the slow paths, not speculatively, and read query plans to verify

• Run online migrations safely (concurrent indexes, dual-writes, backfills, deletion in a separate deploy)

• Know when to denormalize and when to refuse the request to denormalize

Performance and observability: The senior bar is debugging across layers without guessing.

• Profile the slow path wherever it lives: query plan, ORM N+1, serialization, network, downstream service

• Instrument with structured logs, metrics, and distributed traces from the start

• Read Datadog, Grafana, Honeycomb, or comparable tools fluently enough to find the cause, not just the symptom

• Write the runbook before the page wakes someone up

Authentication, authorization, and security: The boundary between client and server is where mistakes happen.

• Choose between session cookies and tokens based on the deployment model

• Implement role-based or attribute-based access control where the product needs it

• Integrate OAuth, SAML, and SSO providers, and rotate secrets without breaking production

• Treat security findings like real bugs, not aspirational tickets

Distributed systems judgment: As soon as the product needs more than one server, the engineer needs more than one mental model.

• Idempotency at the API boundary, exactly-once versus at-least-once choices at the queue boundary

• Distributed transactions, sagas, and event sourcing where they fit the access pattern

• CAP trade-offs picked deliberately, not by default

• Cache invalidation, stale-while-revalidate, and the long tail of consistency bugs

Production operations: Senior backend engineers run their code in production.

• Containerization with Docker and deployment to the team's platform (Kubernetes, ECS, Fly, Render)

• Maintain CI/CD pipelines that the team trusts and that catch real problems

• Take on-call rotations and write the runbooks for the systems they own

Cross-team collaboration: A lot of the work happens outside the editor.

• Partner with product on scope and trade-offs, especially when the database constraints push back on the design

• Partner with front-end engineers on API contracts before they freeze

• Mentor junior engineers through code review and pair programming

The skill bar separating a senior backend developer from a generalist is depth in a few areas, not breadth across all of them. Terminal screens for both. Only the top 7% pass our screening, and the skills below are the ones that come up in technical interviews.

Core programming fluency: Real depth in one strong backend language, plus working competence in a second.

• Python with FastAPI, Django, or Flask; Node.js with Express or NestJS; Go for performance-sensitive services; Java with Spring Boot for enterprise systems; Ruby on Rails when framework speed wins

• The senior tell is comfort with the language's runtime model: event loop, GIL, goroutines, garbage collection, memory management

• Comfort writing concurrent code with the right primitives for the language

API design with judgment: Production experience designing the contract, not just implementing it.

• REST principles, HTTP verb discipline, status code semantics, and content negotiation

• GraphQL schema design and an opinion on when GraphQL is the wrong choice

• gRPC, Protocol Buffers, and binary protocols where they fit

• Versioning strategy that preserves backwards compatibility

Database literacy at depth: Strong SQL is non-negotiable.

• Joins, window functions, common table expressions, and subqueries written without a reference

• Indexing strategy, B-tree versus hash versus partial versus covering indexes, and reading EXPLAIN output

• Online migrations that do not lock production tables

• NoSQL where it fits the access pattern: document, key-value, wide-column, graph, time-series, and vector stores

Distributed systems judgment: Familiarity with the trade-offs at scale.

• Message queues and event streaming: Kafka, RabbitMQ, NATS, SQS, Pub/Sub

• Caching layers and invalidation strategy: Redis, Memcached, application-level caches

• Service mesh, API gateway, and the boundaries between them

• CAP theorem applied to actual product decisions, not whiteboard exercises

Cloud and DevOps fluency: Senior backend engineers ship to a real platform.

• AWS, GCP, or Azure familiarity including IAM, managed databases, and serverless primitives

• Docker for local development and production parity

• CI/CD pipelines configured deliberately, not just consumed

• Basic Kubernetes when the team runs on it; comfort with Helm, kustomize, or platform abstractions

Testing discipline: Knowing what to test is as important as knowing how.

• Unit tests for business logic, integration tests for the API and database boundary

• Contract tests at the service boundary where it matters

• Comfort with pytest, JUnit, RSpec, Jest, or Go's testing package depending on the stack

• Property-based testing or fuzz testing where the input surface is large

AI Fluency: The capability shift that is reshaping engineering output.

• Daily use of Claude Code, Cursor, GitHub Copilot, or comparable AI coding assistants

• Comfort orchestrating agents for refactors, migrations, and test generation, with human-in-the-loop review

• AI Enabled or AI Native tier per Terminal's standard. The engineer either uses AI tools to compound their output significantly, or builds agentic workflows directly

Soft skills that matter: The non-technical bar is real.

• Clear written communication. Most backend work happens in pull requests, design docs, and async threads

• Pragmatism on scope. Knowing when to ship and when to refactor

• Mentorship instinct. Senior engineers raise the floor of the whole team

• Calm under production pressure. The slow query, the failed deploy, the data corruption incident