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Senior Backend Software Engineer

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Senior Backend Software Engineer

3shool Technology Consultants

Senior Backend Software Engineer

Skills

DockerFlaskPythonMicroservicesPostgreSQLMySQLRedisAWSKubernetesFastAPI

1 month ago

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Important information


Contract type:

Permanent

Location:

Hyderabad, Telangana, India

Work mode:

Remote

Published on:

22 September 2026

What they need


Position: Senior Software Engineer 1 Backend (Python)

Role Summary

Owns the core API’s: the largest and most business-critical Python services covering students, users, authentication, organizations, the API gateway, and reporting, plus the central database migrations repository and the legacy LMS_Back monolith. These services run Flask with SQLAlchemy and Alembic, Redis/RQ background jobs, boto3/AWS integrations, and DocuSign e-signature workflows, deployed as Docker containers on EKS.

Key Responsibilities

  • Deliver features across the Python microservices and the reporting stack.
  • Maintain and harden the authentication service and API gateway (JWT issuance, routing, service-to-service auth).
  • Own the DocuSign e-signature integration embedded in flows.
  • Lead the Flask 1.x 2.x/3.x and dependency-CVE remediation program across owned services, coordinated with the Architect's upgrade policy.
  • Manage schema evolution through database_migrations (Alembic) across MySQL and PostgreSQL.
  • Raise the testing bar: most owned services ship without test suites — introduce pytest coverage and CI gates.
  • Operate Redis/RQ background job workloads (croniter scheduling, async processing).
  • Participate in cross-review of the other Python services to reduce bus factor (CODEOWNERS).

Required Qualifications

  • 7+ years backend engineering, 5+ in Python.
  • Expert Flask (or FastAPI with strong Flask willingness), SQLAlchemy 1.x2.x, Alembic.
  • MySQL and PostgreSQL: query tuning, schema design, safe migrations.
  • Redis-backed job queues (RQ, Celery, or equivalent).
  • AWS SDK (boto3): S3, SES/SNS, and service integration patterns.
  • pytest and test-infrastructure buildout in low-coverage codebases.
  • Docker; comfortable deploying to Kubernetes.

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