Senior Full Stack Developer ID71007
On-siteBelo Horizonte, Minas Gerais, Brazil
Job Summary
Build and maintain scalable backend services and responsive frontend interfaces that surface, validate, and resolve issues in data from ETL pipelines, data lakes, and ML model results. Develop REST APIs in Python using FastAPI or Flask, manage PostgreSQL schemas, and package applications with Docker while leveraging AI-assisted tools with critical judgment to ensure long-term maintainability. Drive conversations with engineers to clarify requirements and define problems in ad hoc environments, working daily alongside teams over Slack and Google Meet. This role fits within AgileEngine's Inc. 5000 team, which serves Fortune 500 brands and startups across 17+ industries with a focus on application development and AI/ML.
Required Qualifications
- 5–7 years of full-stack software development experience
- Strong proficiency in Python (FastAPI or Flask) and TypeScript/React
- Solid understanding of relational databases (PostgreSQL) and database migrations
- Comfortable with REST APIs; able to work with and migrate away from legacy GraphQL where needed
- Demonstrated experience building or maintaining data-heavy applications (large datasets, performance-sensitive queries at the application layer)
- Practical, hands-on use of AI-assisted development tools, paired with the critical judgment to challenge AI output when it compromises long-term maintainability
- High comfort level working entirely in a Mac/Linux terminal environment (Bash, Makefiles)
- Strong soft skills (mandatory, not a bonus): the ability to hold and defend a technical opinion — challenging a stakeholder's or a tool's proposed 'quick fix' with sound reasoning in pursuit of a solution that scales and is maintainable long-term, while still being pragmatic enough to ship
- Comfort with ambiguity (mandatory): work is frequently ad hoc and underspecified. This role requires defining the problem — gathering context, identifying constraints, and framing the work — before solving it, rather than waiting for a specification. Experience limited to well-specified work executed through agent workflows is not a fit
- Upper-intermediate English level
Desired Qualifications
- Exposure to object-storage-backed data lakes and serverless query engines (S3 + Athena, or comparable — Trino/Presto, BigQuery, Spark SQL)
- Familiarity with DAG-based task orchestration (Airflow, Dagster, Prefect, Step Functions), or the ability to pick it up quickly
- Data engineering exposure — experience building or maintaining ETL pipelines, advanced SQL (complex queries, data validation, optimization)
- Caching/NoSQL systems (Redis, DynamoDB)
- Monorepo tooling or modern package managers (Nx, Poetry, UV)
- Familiarity with AWS Bedrock or similar AI/LLM SDKs
- Experience with marketing data structures, campaign management APIs, or digital advertising metrics
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