Python Developer

Comprehensive expertise across all phases of the Software Development Lifecycle (SDLC), including requirements analysis, design, development, testing, deployment, and maintenance.Strong backend development experience using one or more of the following languages: Python, PySpark.Proficiency in data modeling, schema design, SQL, and query optimization across relational and non-relational databases, including RedShift, PostgreSQL, and DynamoDB.Hands-on experience with Git-based workflows and familiarity with CI/CD tooling, including Docker, GitLab and Terraform.Extensive experience architecting and implementing cloud-native solutions on AWS, leveraging best practices for scalability, availability, and security.Familiarity with using Microsoft Copilot, ChatGPT or alternate Gen AI tools for code generation, code reviews, applying coding standards, generating unit test cases, document generation, etc. during various phases of development life cycle.Domain knowledge in Mortgage Banking, Bond Markets and/or other financial sectors in trading desk activities, front office trading desk support, financial risk analytics, P&L.Basic understanding of key mortgage trading financial instruments like Mortgage-Backed Securities (MBS), Mortgage Loans, Debt instruments, Derivatives – e.g. Swaps, Swaptions, Futures.Experience:Bachelor’s degree in computer science, information systems or related field.Desired 10+ years software development experience across the appropriate platform.Technical Skills:Backend Development: Python, NodeJS, PySpark, Scripting.Familiar with Tools: IntelliJ, VSCode, DBeaver, Postman.Databases: SQL, DynamoDB, Postgres, RedShift. Write and optimize SQL.AWS Services: Lambda, S3, Step Functions, Glue, EC2, ECS, RDS, CloudWatch, Redshift, AWS CLI, AWS Event Bridge, SNS / SQS.DevOps: CI/CD, Docker, GitLab and Terraform.Familiarity with using Microsoft Copilot, ChatGPT or alternate Gen AI tools for code generation, code reviews, applying coding standards, generating unit test cases, document generation, etc. during various phases of development life cycle.Usage of AWS Bedrock models in implementing Gen AI agents for use cases is a plus.#LI-CGTS#TS-3142