Backend Developer (Python / Data Engineering / GCP)

Casado; São Paulo; Remote; BRL 356,000 - 611,000; Full time

Benefits offered by this job

Remote work

Long-term contract

Job summary

Casado is seeking a Backend Developer focused on Python, data engineering and GCP. You’ll own backend services, data pipelines and cloud infra, while contributing frontend work in React/Next.js and TypeScript.

Expect to work on end-to-end production systems from ingestion to serving, across a data-driven product. You’ll collaborate across data, infrastructure, and product teams, improving performance, scalability and reliability, with daily standups and flexible remote hours aligned to US

Qualifications

• Strong experience building Python backend systems and APIs in production.
• Experience with BigQuery, advanced SQL, and large datasets.
• Production experience with GCP (Compute Engine, Cloud Storage, Pub/Sub).
• Experience building ETL pipelines, background jobs, and asynchronous processing.
• Strong data architecture and debugging skills; frontend exposure welcome.

Responsibilities

• Build and maintain Python backend services and production APIs.
• Design, develop, and optimize ETL pipelines for large datasets.
• Write advanced SQL and optimize BigQuery workloads.
• Manage scheduled jobs, queues, and asynchronous workflows.
• Operate GCP infrastructure and monitor system performance and reliability.
• Contribute frontend changes in React/Next.js/TypeScript when needed.

Skills

Python backend

Data engineering

GCP

SQL

ETL pipelines

React / Next.js

TypeScript

AI coding tools

English communication

Git workflows

Tools

BigQuery

Compute Engine

Cloud Storage

Pub/Sub

GitHub Copilot

Claude

Cursor

Job description

Backend Developer (Python / Data Engineering / GCP)

Location: Remote — LATAM preferred
Commitment: Full-time, 40 hours per week
Schedule: U.S. Central Time business hours
Contract: Long-term contractor
Hiring timeline: Within 2 weeks

About the Role

We’re looking for a Backend Developer whose strongest skills are Python, data engineering, and GCP to take ownership of backend services, data pipelines, and cloud infrastructure. You’ll build and operate systems that turn large volumes of data into fast, reliable information for users. Backend and data engineering will be your primary focus, but you’ll also make frontend changes in React, Next.js, and TypeScript to bring features through to completion.

You should understand the complete flow of a production system:

• Data ingestion
• processing
• storage
• serving
• API
• frontend
• production

For selected projects, you’ll own the full lifecycle: technical planning, implementation, deployment, product analytics, and iteration.

What You’ll Own

• Build and maintain Python backend services and production APIs.
• Design, develop, and optimize ETL pipelines for large historical and geographic datasets.
• Write advanced SQL and optimize BigQuery workloads.
• Manage scheduled jobs, queues, background processes, and asynchronous workflows.
• Operate GCP infrastructure, including Compute Engine, BigQuery, Cloud Storage, Pub/Sub, monitoring, and load balancing.
• Improve how data is precomputed, packaged, cached, compressed, and served.
• Debug issues across source data, pipelines, databases, APIs, infrastructure, and the user-facing application.
• Improve system performance, reliability, scalability, and monitoring.
• Make frontend changes in React, Next.js, and TypeScript, using AI coding tools to work efficiently.
• Ship selected features end-to-end, measure adoption and performance, and iterate after launch.

Must-Haves

• Strong professional experience building Python backend systems and APIs in production.
• Experience with BigQuery, advanced SQL, and large datasets.
• Production experience with GCP, particularly Compute Engine, Cloud Storage, and Pub/Sub.
• Experience building ETL pipelines, background jobs, and asynchronous processing.
• Strong skills in data architecture, system design, performance optimization, and debugging.
• Enough React, JavaScript, or TypeScript experience to contribute productively to an existing frontend codebase.
• Daily use of AI coding tools such as Claude, Cursor, or GitHub Copilot.
• Strong English communication skills for daily standups and one-on-one conversations with leadership.
• Proficiency with Git and modern development workflows.
• Availability for 40 hours per week aligned with U.S. Central Time business hours.

Nice-to-Haves

• Experience with PostHog or other product analytics tools.
• Experience working with geographic or time-series data.
• Experience with caching and data packaging for high-traffic applications.

Who You Are

You’re a hands-on engineer who takes responsibility for how systems perform in production. You’re comfortable writing code, investigating bugs, deploying solutions, and troubleshooting issues across the stack.

You use AI tools regularly to understand unfamiliar code, write tests, refactor systems, and implement frontend changes. You don’t need to be a frontend specialist, but you should be comfortable completing the work required to deliver a usable feature.

You care about what happens after launch: whether users adopt a feature, whether it performs reliably, and what should improve next. You communicate proactively, raise risks early, and recommend practical solutions.

Who This Role Is Best Suited For

Your strongest experience should clearly be in backend development, data engineering, and GCP.

This role is unlikely to be a fit if your background is primarily:

• Data engineering without building APIs or backend services.
• CRUD-focused backend development without substantial data processing experience.
• Frontend development with limited backend and cloud experience.

Working Style

You’ll collaborate closely with leadership and the development team through daily standups, brief daily one-on-ones, and ongoing communication.

Candidates based in LATAM are preferred, ideally in UTC-5 or UTC-6 time zones. Candidates elsewhere may be considered if they can fully commit to U.S. Central Time business hours.