React)

EXL•Hartford, CT

8h•$90,000 - $120,000•Hybrid

About The Position

We’re looking for a Full Stack Software Engineer who enjoys building end-to-end solutions, collaborating with others, and working across backend, frontend, and cloud technologies. The role involves developing backend services and APIs, building responsive user interfaces, working with relational databases, deploying applications to cloud environments, maintaining CI/CD pipelines, and collaborating across product, design, and QA teams. The posting also includes responsibilities for leading medium-sized data science projects, overseeing data science standards and documentation, mentoring junior team members, and collaborating with other departments.

Requirements

4–5 years of professional software engineering experience.
Strong experience with Python and frameworks such as FastAPI or Django.
Solid SQL skills and experience with relational databases.
Hands-on experience with React or similar frontend frameworks.
Familiarity with AWS, Azure, or GCP.
Experience with CI/CD, Jenkins, and Atlassian tools.
Strong understanding of engineering best practices and version control.
Graduate in Data Science, Computer Science, Statistics, or a related field.
3–4 years of experience in data science or data analysis.

Nice To Haves

Experience with Docker or Kubernetes.
Knowledge of microservices architecture.
Exposure to infrastructure-as-code tools such as Terraform or CloudFormation.

Responsibilities

Build and maintain backend services using Python (FastAPI or Django) or Node.js.
Develop RESTful APIs and integrate them with internal and external systems.
Improve backend service performance, reliability, and scalability.
Build responsive user interfaces using React or similar frameworks.
Work with design and product teams to deliver user experiences.
Write SQL queries and support relational database schema design, data modeling, and performance tuning.
Deploy and manage applications in AWS, Azure, or GCP cloud environments.
Build and maintain CI/CD pipelines.
Use Atlassian tools for workflow and documentation.
Participate in code reviews, sprint planning, and architecture discussions.
Collaborate with product, design, and QA teams.
Troubleshoot production issues and contribute to continuous improvement.
Lead medium-sized data science projects.
Oversee data science standards and documentation.
Mentor junior team members.
Collaborate with other departments.