Software Engineer

Software Engineer (Mid-level)

We're looking for a mid-level Software Engineer with roughly 3–10+ years of professional experience to help build, operate, and improve Mirado's internal engineering platforms. You'll work across two related codebases: a GCP-based data warehouse (TypeScript services + BigQuery) and a Next.js internal web application used by operations, sales, and finance. The role spans data pipelines, internal tools, partner integrations, reporting, reconciliation, and cloud infrastructure.

This role is less about writing greenfield code in isolation and more about evolving and operating real systems. We work heavily with agentic coding tools such as Claude Code, Cursor, Copilot-style assistants, automated reviewers, and repository-specific AI rules. Engineering success here comes from understanding systems, guiding generated code, debugging deeply, spotting bad assumptions, validating behavior, and shipping reliable changes.

You should be comfortable reading unfamiliar code, building real user-facing interfaces, using AI tools responsibly, following existing patterns, and turning ambiguous operational problems into working, maintainable solutions.

What You'll Do

• Build and maintain internal tools for operations, sales, reporting, launch workflows, merchant management, billing, reconciliation, alerts, and partner integrations.
• Build and improve React/Next.js interfaces in our internal web app: screens, forms, tables, dialogs, and data visualizations that real teams rely on daily.
• Use agentic coding tools to implement changes quickly while carefully reviewing, testing, and correcting generated code.
• Debug issues across application code, SQL, cloud services, logs, queues, scheduled jobs, infrastructure config, and warehouse tables.
• Work in TypeScript monorepos with Next.js apps, shared packages, server-side APIs, validation schemas, auth/session logic, and deployment scripts.
• Write and maintain BigQuery SQL for reporting, reconciliation, data quality checks, stored procedures, staging logic, and operational analytics.
• Work with the GCP services that back these systems (Cloud Run, BigQuery, Pub/Sub, Cloud SQL, Cloud Scheduler, Cloud Build, IAM, GCS, Secret Manager).
• Integrate with partner APIs and business systems including Google Sheets/Drive, Slack, Attio, QuickBooks/Intuit, SFTP feeds, and email.
• Improve tests, documentation, observability, deployment safety, and repo-specific AI guidance.
• Participate in code review with a strong focus on correctness, production behavior, security, data integrity, and maintainability.

Required Qualifications

• Bachelor's degree in Computer Science or a closely related technical field.
• Approximately 3–10+ years of professional software engineering experience.
• Strong computer science fundamentals and practical debugging ability.
• Strong TypeScript or JavaScript skills across full-stack application code.
• Comfortable building React user interfaces, not just backend code — components, state, hooks, and client/server boundaries.
• Solid SQL experience beyond basic CRUD, preferably in analytical warehouses such as BigQuery, Snowflake, Redshift, or similar.
• Working knowledge of cloud platforms, preferably Google Cloud Platform.
• Familiarity with event-driven systems, queues, pub/sub messaging, scheduled jobs, or asynchronous processing.
• Comfort working in monorepos and modern tooling (pnpm, Turborepo, or similar).
• Ability to read and troubleshoot shell scripts, YAML, Dockerfiles, deployment config, and environment-driven systems.
• Adaptability to an AI-first engineering workflow: able to review generated code critically, identify flawed assumptions, and validate behavior through tests, logs, data checks, and manual inspection.
• Strong written communication, especially for documenting decisions, debugging notes, operational procedures, and AI-agent instructions.

Preferred Experience

• Hands-on experience with agentic tools: Cursor, Claude Code, GitHub Copilot, OpenAI Codex-style agents, automated code review agents, AI-assisted testing, or repository rules for agent guidance.
• Next.js (App Router, React Server Components, route handlers, Turbopack) and React 19.
• Frontend UI work with Tailwind CSS, component libraries (shadcn, Base UI, Radix-style primitives), and data visualization (Recharts or similar); building accessible, responsive internal admin UIs.
• Strict TypeScript, Zod or similar validation libraries, typed contracts, result/error handling patterns, and shared package architecture.
• PostgreSQL, Drizzle ORM, Cloud SQL, migrations, role/capability systems, and internal business data models.
• BigQuery stored procedures, UDFs, audit tables, staging-to-source-of-truth workflows, QA queries, and parameterized analytical SQL.
• Google Cloud Run, Pub/Sub, BigQuery, Cloud Build, IAM, GCS, Secret Manager, Cloud Scheduler, Cloud Workflows.
• Firebase Authentication, Google IAP, and session/entitlement systems.
• Building ETL, ELT, data ingestion, billing, reporting, reconciliation, partner integration, or operational alerting systems.
• REST API integrations, OAuth2/client credentials flows, retry handling, idempotency, rate limits, and failure recovery.
• Python for data tools, scripts, notebooks, or one-off operational workflows.
• Business-system integrations: Google Sheets/Drive, SFTP, Excel generation, Slack, Attio, QuickBooks/Intuit, transactional email (e.g. nodemailer), or Google Maps.
• Testing with Jest or Vitest, and operating production systems: reading logs, tracing failures, validating data, responding to incidents, and improving runbooks.

What Success Looks Like

In your first few months, you'll take well-scoped tickets from investigation through implementation, testing, and operational validation. Examples include adjusting a Next.js internal workflow or screen, updating a Cloud Run handler, modifying a BigQuery query or stored procedure, adding a partner validation step, fixing an auth or entitlement edge case, improving a provisioning script, or debugging a failed scheduled job. You'll use AI agents heavily but lean on your own engineering judgment to validate the results — checking diffs, understanding system impact, and making sure changes fit the repo's patterns.

Over time, you'll grow into owning larger slices of Mirado's internal platform: reporting workflows, partner ingestion flows, ops tooling, reconciliation systems, data quality checks, cloud deployment patterns, and AI-assisted engineering practices.

Ideal Candidate Profile

You enjoy solving problems more than typing code. You like understanding how systems fit together, how data moves, how services fail, and how to make workflows more reliable. You're comfortable using AI to move faster, but you don't blindly trust generated output. You stay engaged with the AI engineering community and enjoy exploring new tools that improve the speed and quality of the team's work.

You can work with mentorship, but you bring enough experience to investigate independently, ask sharp questions, communicate clearly, and leave systems easier to understand than you found them. You don't need to be an expert in every technology we use however you should be strong at learning from an existing codebase, validating your work, and applying sound engineering judgment across unfamiliar terrain.