Senior Full Stack Developer ID71007

Job Description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for a Senior Full Stack Developer to build and maintain backend services and frontend interfaces that surface, validate, and resolve issues in data coming from ETL pipelines, data lake outputs, and ML model results for a large-scale marketing analytics platform. You will develop REST APIs in Python using FastAPI or Flask, build responsive React and TypeScript frontends, manage PostgreSQL schemas and migrations, and leverage AI-assisted development tools with the critical judgment to push back on shortcuts that compromise long-term maintainability. You will work in an environment where requirements are often ad hoc and underspecified, taking ownership of defining the problem, gathering context, identifying constraints, and shaping practical solutions before implementation.

WHAT YOU WILL DO

- Build and maintain scalable backend services (REST APIs) and responsive frontend interfaces (React/TypeScript).

- Build the interfaces and services that surface, validate, and resolve issues in data coming from the data lake, ETL pipelines, health-check logic, or model outputs — without necessarily owning pipeline architecture.

- Package and manage applications using Docker.

- Use AI-assisted development tools (e.g., Claude Code) to move fast during implementation — while critically evaluating AI-generated output and pushing back when it isn't the right long-term solution.

- Drive conversations directly with engineers to clarify both functional and non-functional requirements, rather than defaulting to whatever "just works" in the short term.

- Work daily alongside engineers over Slack and Google Meet.

MUST HAVES

- 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.

NICE TO HAVES

- 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.

PERKS AND BENEFITS

- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget

- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews

- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm

- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands

- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized

- Well-being & support: access local well-being programs and people-focused support tailored to your location

Requirements
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): Kepler's 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.