Talently
Talently
Software Architect

Software Architect

Defines the technical structures that allow systems to scale, evolve, and endure over time at the lowest possible cost.

A Software Architect is responsible for making and communicating the high-impact technical decisions that define the structure of software systems: what components they are made of, how those components communicate, what technologies underpin them, and how they will evolve over time. Their work transcends individual code: it defines the constraints and patterns within which development teams make their day-to-day decisions. They collaborate with technical leads, CTOs, product managers, and business stakeholders to align architectural decisions with business objectives, constraints, and risks.

MicroservicesDomain-Driven DesignCloud ArchitectureDesign PatternsAPI DesignSecurity

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Main Responsibilities

  • •Define and document the architecture of new systems and the evolution of existing ones using Architecture Decision Records and C4 diagrams.
  • •Identify and manage high-impact technical risks: single points of failure, critical technical debt, and irreversible decisions.
  • •Establish the technical principles, patterns, and standards that guide implementation decisions across development teams.
  • •Evaluate and recommend technologies, frameworks, and platforms based on technical and business merit — not novelty.
  • •Collaborate with the technical leads of each team to ensure that implementation remains consistent with the defined architecture.
  • •Participate in the technical planning of product initiatives by assessing the architectural impact of new requirements.

Key Skills

Technical Skills

  • Distributed architecture design: microservices, event-driven architecture, CQRS, Event Sourcing, and their real-world trade-offs
  • Domain-Driven Design to identify bounded contexts, define the ubiquitous language, and structure teams around business domains
  • API and integration contract design: REST, GraphQL, gRPC, and versioning and evolution strategies
  • Cloud-native architectures on AWS, GCP, or Azure with deep knowledge of compute, networking, messaging, and storage services
  • Security by design: threat modeling, least privilege, zero trust, and encryption at every layer
  • Resilience patterns: circuit breakers, bulkheads, retry with backoff, saga pattern, and graceful degradation strategies

Soft Skills

  • Ability to communicate complex technical decisions at varying levels of abstraction depending on the audience: CTO, developers, or business stakeholders
  • Influence without direct authority: driving architectural adoption through conviction, not mandate
  • Systems thinking to anticipate how a local decision impacts the global system in the short, medium, and long term
  • Technical humility to acknowledge when a prior architectural decision was wrong and propose its evolution with supporting data
  • Judgment to distinguish between problems that require architectural decisions and those teams should resolve autonomously
  • Ambiguity management: making well-grounded technical decisions when requirements are not yet fully defined

Real use cases

Context

Decisions made in the first weeks of a system define the costs and constraints of the years ahead. A well-considered initial architecture does not eliminate change — it makes change less expensive.

Real examples

  • Bounded context definition and interface design using Domain-Driven Design
  • Architecture option evaluation with Decision Records documenting the reasoning
  • Data ownership strategy design: which data belongs to which service and how it flows
  • Integration contract definition between services before teams begin implementation

Context

Most architects work on existing systems, not blank slates. Incremental modernization is more valuable and less risky than a full rewrite.

Real examples

  • Applying the strangler fig pattern to extract modules from the monolith without disrupting the business
  • Identifying and prioritizing technical debt with the greatest impact on development velocity
  • Incremental migration from a monolithic architecture to well-bounded services
  • Designing an anti-corruption layer to integrate the legacy system with new services without coupling the domains

Context

In organizations with multiple development teams, architectural standards ensure consistency, reduce collaboration friction, and prevent the proliferation of incompatible solutions.

Real examples

  • Defining golden paths: recommended toolsets and patterns for common use cases
  • API design guidelines with standards for naming, pagination, error handling, and versioning
  • Architecture Decision Record process to document and communicate cross-cutting technical decisions
  • Architecture reviews for new projects before implementation begins

Context

Distributed systems fail in complex and not always predictable ways. The architect must identify risks before they materialize and design mitigation mechanisms.

Real examples

  • Single point of failure mapping and redundancy and failover strategy design
  • Chaos engineering exercises to validate that resilience mechanisms work as expected
  • Per-service SLO definition and architecture design to meet them within the available error budget
  • Threat modeling to identify attack surfaces and design preventive controls

Context

Every new technology added to the stack is a knowledge, operational, and maintenance liability. Rigorous evaluation prevents the uncritical proliferation of tools.

Real examples

  • Internal technology radar classifying technologies into adopt, trial, hold, and avoid
  • Technical spikes with predefined evaluation criteria before adopting a technology in production
  • Total cost of ownership evaluation: not just the adoption cost but the operational cost and future migration cost
  • RFC process for new technology adoption proposals that involves the affected teams

Basic questions

Start with a well-structured monolith in most cases: lower operational complexity, easier to refactor when bounded contexts are not yet clear, and sufficient for most early-stage products. Migrate to microservices when the monolith has real, measurable problems: teams blocking each other, the need for independent scaling by module, or incompatible technologies across domains. The complexity of microservices must be justified by concrete problems, not by aspirations of future scale.
An ADR documents the problem context, the options considered with their pros and cons, the decision made, and the anticipated consequences. It is more valuable than just the decision because: it explains why alternatives were rejected (avoiding re-evaluating the same options in the future), communicates the constraints that existed at the time, and allows the decision to be revisited when the context changes. An ADR is a communication tool as much as a documentation artifact.
An architectural decision is needed when: the decision is hard to reverse, it affects multiple teams or systems, it has security, compliance, or cost-at-scale implications, or it establishes a pattern that other teams will follow. Teams resolve autonomously when: the decision is within a single service's boundaries, it is technically reversible, and it does not affect contracts with other systems. An architect who makes every technical decision becomes a bottleneck; one who makes none leaves teams without coherence.
High cohesion: each service or module contains logic that changes together and for the same reasons (the same business domain). Low coupling: services depend on stable interfaces, not on the internal implementation details of other services. In practice: a payments service must not know the internal structure of the users service — it communicates through an API contract or events. High coupling causes a change in one service to require coordinated changes in others, which is the primary driver of slowness in distributed architectures.
Define evaluation criteria before evaluating: maturity and stability, community and long-term support, integration with the existing stack, operational cost and future migration cost, team learning curve. Run a time-boxed technical spike using a real product use case, not a hello world. Evaluate not only whether it solves the current problem but whether it creates new ones. Adoption must be justified by evidence that it solves the problem better than alternatives already in the stack.
Threat modeling early to identify the most relevant attack surfaces. Least privilege at every level: services, databases, users. Encryption in transit and at rest for sensitive data. Authentication and authorization as cross-cutting layers, not as the individual responsibility of each service. Network segmentation so services can only communicate with those they need to. Security by design is exponentially cheaper than security added after the fact.
Connect the decision to a concrete business risk or cost it prevents: not as 'it is best practice' but as 'without this change, in six months we will face this measurable problem'. Show the cost of not doing it with data or analogies from the system itself. Involve the team in designing the solution so they are part of the decision, not just recipients of it. Propose an incremental implementation that distributes the additional work over time without blocking value delivery.
A bounded context is an explicit boundary within which a specific domain model is valid and consistent. The same concept can have different meanings in different bounded contexts: 'customer' in the sales context has different attributes than in the support context. Service boundaries should align with bounded context boundaries so each service has a coherent domain model and does not depend on the internals of another's model. When two services share the same model, there is domain coupling that complicates independent evolution.

Technical questions

Microservices win on: independent deployment per service, granular scaling, fault isolation, and the ability to use different technologies per domain. They lose on: operational complexity (N services to monitor, deploy, and operate), network latency in inter-service communication, distributed data consistency (no ACID transactions across service boundaries), and more complex distributed observability. A well-structured modular monolith captures many of the design benefits of microservices without the operational overhead. The most underestimated trade-off is the human cost: microservices require DevOps maturity that many teams do not have.
Without distributed transactions — two-phase commit is fragile and creates tight coupling. The saga pattern manages eventual consistency: each service executes its local transaction and emits an event; the next service reacts to the event. If a step fails, compensating transactions are executed to undo the previous steps. Orchestrated sagas have a central coordinator controlling the flow; choreographed sagas have each service reacting to the others' events. Orchestration is easier to monitor and debug; choreography has less coupling but more traceability complexity.
CQRS separates the write model (commands that modify state) from the read model (queries that retrieve state), with data models optimized for each. It is justified when: the optimal write model (normalized, with business validations) is incompatible with the optimal read model (denormalized, pre-aggregated). It introduces complexity: two data models to keep in sync, eventual consistency between write and read, and a larger code surface. Do not adopt by default: most CRUD systems do not benefit from CQRS and carry its complexity unnecessarily.
Core principle: never introduce breaking changes in an existing version. Breaking changes are: removing fields, changing types, changing the semantics of existing fields, or removing endpoints. Non-breaking: adding optional fields, adding new endpoints. When a breaking change is necessary: create a new version (/v2/) and maintain the previous version with a communicated deprecation period. Use consumer-driven contract testing (Pact) to know exactly which consumers use which part of the contract before removing anything.
The circuit breaker has three states: closed (normal traffic), open (the service is failing — traffic is cut and a fallback response is returned immediately), and half-open (a fraction of traffic is allowed through to verify whether the service has recovered). Configuration requires three decisions: the failure threshold to open the circuit (e.g., 50% errors in the last 10 seconds), the time it stays open before attempting half-open, and the fallback behavior (cached response, default response, or explicit error). Resilience4j for the JVM and Polly for .NET are mature implementations. Values must be calibrated against the service's real production behavior.
Every service must authenticate itself to the services it consumes — not just end users to the system. Mutual TLS (mTLS) between services verifies identity in both directions. A service mesh such as Istio or Linkerd manages mTLS transparently without modifying application code. Granular authorization: service A can read from service B but not write to it. Audit logs of all inter-service communication. In cloud environments: IAM roles for services instead of static credentials. Zero trust increases configuration surface area — a policy error can cause outages, so rollout must be incremental.
Event-driven is appropriate when: multiple consumers need to react to the same event without the producer knowing them, temporal decoupling is needed (the producer does not wait for consumers to process), or the event audit trail has its own business value. It is not appropriate when: an immediate synchronous response is required (direct request-response is simpler), the event volume is low and the broker complexity is not justified, or the team has no experience operating messaging systems. The largest hidden cost is observability: tracing a business flow across multiple asynchronous consumers is significantly more complex than in a synchronous system.
The three pillars are each necessary but insufficient alone: structured logs with a correlation ID that flows through every service, metrics with latency percentiles (p95, p99) and error rates per service and endpoint, and distributed traces with OpenTelemetry showing the complete path of a request. For diagnosis in 15 minutes: metrics show there is a problem and in which service, traces show in which specific operation it fails, and logs provide the detailed error context. Without all three, diagnosis is iterative and slow. The correlation ID is the thread that connects all three.

Advanced questions

At that scale, every architectural decision has a measurable SLA impact. Eliminate latency at every layer: aggressive in-memory caching to avoid I/O, asynchronous processing for operations not on the critical response path, data partitioning to distribute write load. Design for horizontal scale: stateless application instances, minimal coordination between nodes. A p99 of 50ms requires that the p99 of every component in the path be a fraction of that total. Profile the system under real load before optimizing: assumptions about the bottleneck are frequently wrong without data.
Make the debt visible with concrete impact metrics: feature development velocity slowed by the debt, number of incidents related to high-debt components, operational cost of legacy systems. Propose a technical debt budget model: a fixed percentage of engineering capacity (20% is a common starting point) dedicated to debt reduction in every sprint, not negotiable per feature. The most effective argument is not technical but economic: how much slower is feature delivery because of accumulated debt. Ignored architectural debt compounds and eventually paralyzes the organization.
Separate decisions by impact level: low-impact ones (within a single service, reversible) are made autonomously by teams within established guardrails. Medium-impact ones (new service, new technology) go through a lightweight review with an ADR. High-impact ones (platform changes, new architectural style, irreversible decisions) go through an Architecture Review Board. Publish ADRs and standards so teams can make informed autonomous decisions. The architect as consultant and reviewer — not as approver of everything.
Data migration is the riskiest step in monolith decomposition. Strategy: first identify which tables belong to which domain — some will be shared, which is the highest-friction point. For tables clearly owned by one domain: create the new service reading from the monolith's database via a controlled access layer, then migrate the data to the service's own database, then cut the dependency. For tables shared across domains: define which domain owns the data, create access APIs for other domains, and eventually replicate only the needed data. Dual-write synchronization during the transition increases complexity but is essential for safe rollback.
DDD at scale requires prerequisites that go beyond the technical. Organizational: teams must align with business bounded contexts, not with technical layers. Domain experts must actively participate in model design — not just validate at the end. The business must commit to the ubiquitous language: if the code uses different terms than the business, DDD is not being applied. Technical: teams must have experience with DDD tactics (aggregates, value objects, domain events) before applying strategic patterns. The greatest risk is applying DDD as a technical exercise without the organizational commitment it requires.
A centralized identity provider (Okta, Auth0, Keycloak, or custom-built) as the source of identity truth, with OIDC/OAuth2 support for SSO across products. Issued tokens carry basic identity claims; granular authorization (what the user can do in each product) is evaluated locally at each service using the token claims plus the context of the requested resource. This prevents the IDP from becoming a bottleneck carrying each product's business logic. For independent teams: define the minimum claims contract all products consume, and let each product extend with its own claims. The onboarding process for new products onto the SSO must be self-service with clear documentation.

Common interview mistakes

Microservices solve real problems at scale but introduce operational, distributed data, and observability complexity that many teams are not equipped to manage. An architect who proposes microservices without evaluating team size, DevOps maturity, and domain complexity is following technology fashion, not exercising architectural judgment.
Every architectural decision involves trade-offs. An architect who describes past decisions as obviously correct without mentioning what was sacrificed, which alternatives were rejected, and why does not demonstrate the critical thinking the role requires. Senior interviewers always ask what you would do differently today.
Conway's Law establishes that systems tend to mirror the communication structure of the organizations that build them. An architect who designs without considering how teams are organized will produce an architecture the team cannot implement naturally. Architecture and team organization must be designed together.
A diagram without the reasoning behind it is decoration. Architecture-level interviewers ask why each element in the diagram is there, what alternatives were considered, and what the known risks are. An architect who can only draw boxes and arrows without articulating the decisions that justify them is not fulfilling the most valuable function of the role.
A beautiful architecture on paper that is impossible to operate in production has no value. The cost of operating the architecture (monitoring, deployment, recovery) must be part of the design from the start. An architect who does not ask how the system will be operated before designing it is transferring operational debt to the infrastructure team.
Event Sourcing, CQRS, distributed sagas, and other advanced patterns solve specific problems at scale. Applying them to systems that do not have those problems adds complexity without benefit. A mature architect can articulate exactly which specific problem of the system justifies adopting each complex pattern, and what simpler system would also solve the problem.

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Aplazo
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We are looking for a Arquitecto de Software. Knowledge required in Java, Python, Angular.

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