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Backend Engineering
A senior backend engineer's decision-making framework for building production
systems. This skill covers the six pillars of backend engineering - schema design,
scalable systems, observability, performance, security, and API design - with an
emphasis on when to use each pattern, not just how. Designed for mid-level
engineers (3-5 years) who know the basics and need opinionated guidance on
trade-offs.
When to use this skill
Trigger this skill when the user:
Designs a database schema or plans a migration
Chooses between monolith vs microservices or evaluates scaling strategies
Sets up logging, metrics, tracing, or alerting
Diagnoses a performance issue (slow queries, high latency, memory pressure)
Implements authentication, authorization, or secrets management
Designs a REST, GraphQL, or gRPC API
Needs retry, circuit breaker, or idempotency patterns
Plans data consistency across services (sagas, outbox, eventual consistency)
DevOps/infra provisioning (use a Terraform/Docker/K8s skill instead)
Key principles
Design for failure, not just success - Every network call can fail. Every
disk can fill. Every dependency can go down. The question is not "will it fail"
but "how does it degrade?" Design graceful degradation paths before writing the
happy path.
Observe before you optimize - Never guess where the bottleneck is. Instrument
first, measure second, optimize third. A 10ms query called 1000 times matters more
than a 500ms query called once.
Simple until proven otherwise - Start with a monolith, a single database, and
synchronous calls. Add complexity (microservices, queues, caches) only when you
have evidence the simple approach fails. Every architectural boundary is a new
failure mode.
Secure by default, not by afterthought - Auth, input validation, and encryption
are not features to add later. They are constraints to build within from day one.
Use established libraries. Never roll your own crypto.
APIs are contracts, not implementation details - Once published, an API is a
promise. Design from the consumer's perspective inward. Version explicitly. Break
nothing silently.
Core concepts
Backend engineering is the discipline of building reliable, performant, and secure
server-side systems. The six pillars form a hierarchy:
Schema design is the foundation - get the data model wrong and everything built
on top inherits that debt. Scalable systems define how components communicate and
grow. Observability gives you eyes into what's actually happening in production.
Performance is the art of making it fast after you've made it correct.
Security is the set of constraints that keep the system trustworthy. API design
is the surface area through which consumers interact with all of the above.
These pillars are not independent. A bad schema creates performance problems. Poor
observability makes security incidents invisible. A poorly designed API forces clients
into patterns that break your scaling strategy. Think of them as a connected system,
not a checklist.
Common tasks
Design a database schema
Start from access patterns, not entity relationships. Ask: "What queries will this
serve?" before drawing a single table.
Write-heavy, high throughput -> Consider denormalization, append-only tables
Complex relationships with traversals -> Consider a graph model
Unstructured/evolving data -> Document store (but think twice)
Indexing rule of thumb: Index columns that appear in WHERE, JOIN, and ORDER BY.
A composite index on (a, b, c) serves queries on (a), (a, b), and (a, b, c)
but NOT (b, c). Check the references/ file for detailed indexing strategies.
Always plan migration rollbacks. A deploy that adds a column is safe. A deploy that
drops a column is a one-way door. Use expand-contract migrations for breaking changes.
Choose a scaling strategy
Is a single server sufficient?
YES -> Stay there. Optimize vertically first.
NO -> Is the bottleneck compute or data?
COMPUTE -> Horizontal scale with stateless services + load balancer
DATA -> Is it read-heavy or write-heavy?
READ -> Add read replicas, then caching layer
WRITE -> Partition/shard the database
Only introduce microservices when you have: (a) independent deployment needs,
(b) different scaling profiles per component, or (c) team boundaries that demand it.
Never split a monolith along technical layers (API service, data service). Split
along business domains (orders, payments, inventory).
Set up observability
Implement the three pillars with correlation:
Pillar
What it answers
Tool examples
Logs
What happened?
Structured JSON logs with correlation IDs
Metrics
How is the system performing?
RED metrics (Rate, Errors, Duration)
Traces
Where did time go?
Distributed traces across service boundaries
Define SLOs before writing alerts. An SLO like "99.9% of requests complete in <200ms"
gives you an error budget. Alert when the burn rate threatens the budget, not on every
spike.
Diagnose a performance issue
Follow this checklist in order:
Check metrics - is it CPU, memory, I/O, or network?
Check slow query logs - are there N+1 patterns or full table scans?
Check connection pools - are connections exhausted or leaking?
Check external dependencies - is a downstream service slow?
Profile the code - only after ruling out infrastructure causes
The fix for "the database is slow" is almost never "add more database." It's
usually: add an index, fix an N+1, or cache a hot read path.
Secure a service
Minimum security checklist for any backend service:
Authentication: Use OAuth 2.0 / OIDC for user-facing, API keys + HMAC for
service-to-service. Never store plain-text passwords (bcrypt/argon2 minimum).
Authorization: Implement at the middleware level. Default deny. Check
permissions on every request, not just at the edge.
Input validation: Validate at system boundaries. Use allowlists, not blocklists.
Parameterize all SQL queries.
Secrets: Use a secrets manager (Vault, AWS Secrets Manager). Never commit
secrets to git. Rotate regularly.
Transport: TLS everywhere. No exceptions.
Design an API
REST decision table:
Need
Pattern
Simple CRUD
REST with standard HTTP verbs
Complex queries with flexible fields
GraphQL
High-performance internal service calls
gRPC
Real-time bidirectional
WebSockets
Event notification to external consumers
Webhooks
Pagination: Use cursor-based for large/changing datasets, offset-based only for
small/static datasets. Always include a next_cursor field.
Versioning: URL path versioning (/v1/) for public APIs, header versioning for
internal. Never break existing consumers silently.
Rate limiting: Token bucket for user-facing, fixed window for internal. Always
return Retry-After headers with 429 responses.
Handle partial failures
When services depend on other services, failures cascade. Use these patterns:
Retry with exponential backoff + jitter - for transient failures (network blips,
503s). Cap at 3-5 retries.
Circuit breaker - stop calling a failing dependency. States: closed (normal) ->
open (failing, fast-fail) -> half-open (testing recovery).
Idempotency keys - make retries safe. Every mutating operation should accept
an idempotency key so duplicate requests produce the same result.
Timeouts - always set them. A missing timeout is an unbounded resource leak.
Plan data consistency
For distributed data across services:
Strong consistency needed? -> Single database, ACID transactions
Can tolerate eventual consistency? -> Event-driven with outbox pattern
Multi-step business process? -> Saga pattern (prefer choreography over
orchestration for simple flows, orchestration for complex ones)
The outbox pattern: write the event to a local "outbox" table in the same
transaction as the data change. A separate process publishes outbox events to
the message broker. This guarantees at-least-once delivery without 2PC.
Only load a references file if the current task requires it - they are long and
will consume context.
Related skills
When this skill is activated, check if the following companion skills are installed.
For any that are missing, mention them to the user and offer to install before proceeding
with the task. Example: "I notice you don't have [skill] installed yet - it pairs well
with this skill. Want me to install it?"
