AgileEngine
Senior Backend Engineer ID84684
Ciudad de México, MXPresencialPermanenteTempo integral
Publicado 24 de ago. de 2026
Esta vaga foi publicada em ES
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 Backend Engineer to build and maintain the application services and REST APIs that surface creator insights and AI-generated scores for a data analytics platform, integrating third-party sources including YouTube Creator APIs and Google Trends at the application layer. You will develop Python services using FastAPI and SQLAlchemy with rigorous static typing, manage background task processing with Celery and Redis on EKS, implement Auth0 and ReBAC authorization flows, and partner with a dedicated data engineer on the S3/Glue/Athena pipeline and AWS Bedrock-based scoring layer.
WHAT YOU WILL DO
- Application Development: Build and maintain scalable backend services and the REST APIs the front-end consumes — including the endpoints that expose creator insights, scoring results, and health/status data.
- API Integration: Integrate third-party and internal sources (YouTube Data/Creator APIs, Google Trends, GQV via MMM Data Hub) at the application layer, handling auth, rate limits, pagination, and failure modes.
- Containerized Services: Build and maintain containerized Python services and background workers (RQ/Celery with Redis) running on EKS.
- AWS Serverless Systems: Experience developing and testing Lambda, Step Functions, Containerized systems like Fargate and/or Beanstalk.
- Security Implementation: Implement authentication and authorization flows using Auth0, OpenFGA (ReBAC), and an internal Python RBAC library.
- Data & AI Layer Support: Partner with the dedicated data engineer on the S3/Glue/Athena pipeline and the Bedrock-based Claude/Gemini scoring orchestration layer — consuming their outputs, and stepping in to contribute to that work when capacity or timelines require it.
- AI-Accelerated Implementation: Use Claude Code (pointed at AWS Bedrock) to move fast during implementation — while critically evaluating AI-generated output and pushing back when it isn't the right long-term solution.
- Stakeholder Alignment: Drive conversations directly with engineers to clarify both functional and non-functional requirements, rather than defaulting to whatever "just works" in the short term.
- Synchronous Collaboration: Work daily alongside engineers over Slack and Google Meet.
MUST HAVES
- 5–7 years of backend software development experience, with a strong track record building and maintaining production application services.
- Strong Python proficiency — FastAPI, SQLAlchemy, and modern tooling (uv) — with static typing applied rigorously throughout, not selectively.
- Demonstrated experience designing and building REST APIs consumed by a front-end, including versioning, contracts, and error semantics.
- Solid experience integrating third-party APIs and reconciling data from heterogeneous sources.
- Solid understanding of relational databases (PostgreSQL) and database migrations.
- Hands-on experience with asynchronous/background task processing (Celery, RQ, or comparable) and Redis.
- Comfortable with containerized deployment (Docker) and working against Kubernetes/EKS.
- Enough familiarity with object-storage-backed data lakes and serverless query engines (S3/Glue/Athena, or comparable) to query them, reason about how data reaches the application, and collaborate credibly with the data engineer.
- Comfortable calling LLM APIs from application code and reasoning about the constraints of a managed platform like AWS Bedrock — deep orchestration or ML experience not required.
- High comfort level working entirely in a Mac/Linux terminal environment (Bash, Makefiles).
- Practical, hands-on use of AI-assisted development tools, paired with the critical judgment to challenge AI output when it compromises long-term maintainability.
- 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.
- Upper-intermediate English level.
NICE TO HAVES
- Direct experience with AWS Bedrock, Glue, or Athena.
- Deeper data engineering exposure — ETL pipeline construction, advanced SQL, DAG-based orchestration — enough to take pipeline work off the data engineer's plate when useful.
- Prompt engineering or LLM evaluation experience (scoring, structured output, reliability).
- Familiarity with Auth0, OpenFGA, or ReBAC/fine-grained authorization patterns.
- Experience with YouTube/creator APIs, Google Trends, or marketing/advertising data structures.
- Exposure to Terraform, GitHub Actions, ArgoCD, or Atlantis.
- Enough TypeScript/React familiarity to reason about front-end consumption of the APIs they build.
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
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 Backend Engineer to build and maintain the application services and REST APIs that surface creator insights and AI-generated scores for a data analytics platform, integrating third-party sources including YouTube Creator APIs and Google Trends at the application layer. You will develop Python services using FastAPI and SQLAlchemy with rigorous static typing, manage background task processing with Celery and Redis on EKS, implement Auth0 and ReBAC authorization flows, and partner with a dedicated data engineer on the S3/Glue/Athena pipeline and AWS Bedrock-based scoring layer.
WHAT YOU WILL DO
- Application Development: Build and maintain scalable backend services and the REST APIs the front-end consumes — including the endpoints that expose creator insights, scoring results, and health/status data.
- API Integration: Integrate third-party and internal sources (YouTube Data/Creator APIs, Google Trends, GQV via MMM Data Hub) at the application layer, handling auth, rate limits, pagination, and failure modes.
- Containerized Services: Build and maintain containerized Python services and background workers (RQ/Celery with Redis) running on EKS.
- AWS Serverless Systems: Experience developing and testing Lambda, Step Functions, Containerized systems like Fargate and/or Beanstalk.
- Security Implementation: Implement authentication and authorization flows using Auth0, OpenFGA (ReBAC), and an internal Python RBAC library.
- Data & AI Layer Support: Partner with the dedicated data engineer on the S3/Glue/Athena pipeline and the Bedrock-based Claude/Gemini scoring orchestration layer — consuming their outputs, and stepping in to contribute to that work when capacity or timelines require it.
- AI-Accelerated Implementation: Use Claude Code (pointed at AWS Bedrock) to move fast during implementation — while critically evaluating AI-generated output and pushing back when it isn't the right long-term solution.
- Stakeholder Alignment: Drive conversations directly with engineers to clarify both functional and non-functional requirements, rather than defaulting to whatever "just works" in the short term.
- Synchronous Collaboration: Work daily alongside engineers over Slack and Google Meet.
MUST HAVES
- 5–7 years of backend software development experience, with a strong track record building and maintaining production application services.
- Strong Python proficiency — FastAPI, SQLAlchemy, and modern tooling (uv) — with static typing applied rigorously throughout, not selectively.
- Demonstrated experience designing and building REST APIs consumed by a front-end, including versioning, contracts, and error semantics.
- Solid experience integrating third-party APIs and reconciling data from heterogeneous sources.
- Solid understanding of relational databases (PostgreSQL) and database migrations.
- Hands-on experience with asynchronous/background task processing (Celery, RQ, or comparable) and Redis.
- Comfortable with containerized deployment (Docker) and working against Kubernetes/EKS.
- Enough familiarity with object-storage-backed data lakes and serverless query engines (S3/Glue/Athena, or comparable) to query them, reason about how data reaches the application, and collaborate credibly with the data engineer.
- Comfortable calling LLM APIs from application code and reasoning about the constraints of a managed platform like AWS Bedrock — deep orchestration or ML experience not required.
- High comfort level working entirely in a Mac/Linux terminal environment (Bash, Makefiles).
- Practical, hands-on use of AI-assisted development tools, paired with the critical judgment to challenge AI output when it compromises long-term maintainability.
- 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.
- Upper-intermediate English level.
NICE TO HAVES
- Direct experience with AWS Bedrock, Glue, or Athena.
- Deeper data engineering exposure — ETL pipeline construction, advanced SQL, DAG-based orchestration — enough to take pipeline work off the data engineer's plate when useful.
- Prompt engineering or LLM evaluation experience (scoring, structured output, reliability).
- Familiarity with Auth0, OpenFGA, or ReBAC/fine-grained authorization patterns.
- Experience with YouTube/creator APIs, Google Trends, or marketing/advertising data structures.
- Exposure to Terraform, GitHub Actions, ArgoCD, or Atlantis.
- Enough TypeScript/React familiarity to reason about front-end consumption of the APIs they build.
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
Resumo da função
Tipo de vaga
Tempo integral
Competências necessárias
Python (strong proficiency with rigorous static typing)FastAPISQLAlchemyREST API design, versioning, and error semanticsThird-party API integration (auth, rate limits, pagination, failure handling)Background task processing (Celery, RQ) and RedisContainerization and Kubernetes/EKS deployment (Docker, EKS)AWS serverless technologies (Lambda, Step Functions, Fargate/Beanstalk)Authentication & authorization implementation (Auth0, OpenFGA/ReBAC, RBAC patterns)Relational databases (PostgreSQL) and database migrationsObject-storage-backed data lakes and serverless query engines (S3/Glue/Athena)Calling LLM APIs and integrating managed LLM platforms (AWS Bedrock)Unix/Mac terminal proficiency (Bash, Makefiles)Technical stakeholder communication and persuasion (defend technical decisions, align requirements)Practical use of AI-assisted development tools with critical evaluation
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