Senior Software Engineer - Python, FastAPI, AWS, Kafka

Yogesh Sanjay Bavale

Software Engineer

•*************@*****.*** • +1-551-***-**** • GitHub

SUMMARY

● Software Engineer with 4+ years of experience designing and building scalable web applications and backend services, primarily leveraging Python (FastAPI) for high-performance API development, microservices architecture, and system optimization in Agile environments.

● Skilled in developing full-stack applications, creating responsive user interfaces with React.js, Next.js, and TypeScript, and integrating robust backend services using FastAPI and REST APIs to deliver end-to-end web solutions.

● Experienced in building cloud-native applications utilizing AWS infrastructure, Docker for containerization, and implementing CI/CD pipelines (GitHub Actions, Jenkins) for streamlined, automated deployments.

● Proficient in designing, managing, and optimizing relational databases (PostgreSQL, MySQL), and accelerating data pipeline efficiency by automating ETL workflows with Python and Apache Airflow.

● Skilled in building secure, event-driven systems using Apache Kafka and REST APIs to enable reliable communication, low-latency processing, and secure data flow within distributed architectures.

PROFESSIONAL EXPERIENCE

Northern Trust Corp.May 2024 – Present Remote, USA Senior Software Engineer (Contract)

●Architected and built a modern Identity Manager (IDM) application using Apache Kafka and PostgreSQL to replace a legacy monolith, increasing provisioning throughput by 40% and reducing operational expenditure by 30%..

●Wrote structured, industry-standard code using Python (FastAPI) and modern C++ to build a scalable, cross-platform provisioning engine compatible with both modern cloud APIs and resource-constrained legacy mainframes.

●Engineered high-throughput data solutions to store, retrieve, and manipulate millions of identity events, optimizing PostgreSQL performance via aggressive indexing, read-replicas, and connection pooling to decrease retrieval latency by 35%.

●Secured the client entry point by deploying a centralized API Gateway to handle authentication, enforce rate limiting, and route self-service portal requests, protecting the core microservices from traffic spikes and unauthorized UI access.

●Engineered a custom Reconciliation (Recon) microservice to manipulate and compare state data between legacy banking systems and the core PostgreSQL database, actively detecting access drift and visualizing ‘Inappropriate Access’ accounts via Power BI to ensure continuous SOX compliance.

●Partnered with audit and development teams to enforce strict Role-Based Access Control (RBAC) and Privileged Access Management (PAM) policies, automatically evaluating and restricting elevated access requests in real-time.

●Documented rigorous test plans and audit procedures—including WORM storage validation and SHA-256 feed file hashing—while actively researching new distributed system patterns to solve complex engineering challenges.

●Led Agile sprint planning and daily stand-ups for a cross-functional team of 4 engineers, ensuring on-time delivery of identity governance features. Accenture Jun 2021 – Aug 2022 Pune, India

Software Engineer

●Boosted platform performance by optimizing cloud infrastructure on AWS, utilizing auto-scaling and load-balancing techniques, leading to a 40% increase in service uptime. This change ensured optimal performance during peak traffic periods, supporting real-time financial data processing.

●Accelerated data pipeline efficiency by automating ETL workflows with Python and Apache Airflow, reducing manual processing time by 60%. This automation enhanced data accuracy, enabling timely insights that supported faster decision-making across business units.

●Revamped the user interface with React.js and mobile-first design principles, improving page load time by 25% and enhancing the user experience across devices. This design resulted in a significant increase in user engagement, particularly during periods of high traffic.

●Upgraded backend service reliability by optimizing FastAPI endpoints, reducing response times by 30%. This enhancement ensured high performance during critical usage periods, boosting overall system responsiveness and user satisfaction.

●Elevated frontend performance by implementing Cypress for comprehensive testing, streamlining UI functionality and reducing errors. This resulted in faster load times and a more responsive user experience during high-demand periods.

●Reinforced deployment workflows by integrating Jenkins with Docker, automating code testing and deployment, cutting deployment times by 40%. This enabled faster delivery of updates and improvements, ensuring minimal disruption and more frequent feature releases. SKILLS

Languages & Frameworks: Python, Javascript/TypeScript, C++, Node.js, FastAPI, RESTful APIs, Microservices Architecture Frontend Development: React.js, HTML5/CSS3, Responsive Web Design Cloud & DevOps: AWS (EC2, S3, Lambda, RDS, API Gateway), Apache Kafka, Docker, Kubernetes, Jenkins, CI/CD Pipelines Databases & Data Management: MySQL, PostgreSQL, MongoDB, Redis, Data Modeling, SQL Optimization, Caching (Redis) Version Control & Tools: Git, GitHub, GitLab, Bitbucket, npm Security & IAM Governance: Identity Governance and Administration (IGA), SOX Compliance, Role-Based Access Control (RBAC), API Gateway, OAuth2, JWT, WORM Storage, Cryptographic Hashing (SHA-256) Testing & Performance: JUnit, Jest, Cypress, Performance Tuning Agile & Collaboration: Agile (Scrum/Kanban), JIRA, Confluence, Code Reviews, Sprint Planning, Cross-Functional Teamwork EDUCATION

Master of Science in Information Systems Sep 2022 – May 2024 New York City, NY Pace University

PERSONAL & ACADEMIC PROJECTS

Quant Finance Research and System Implementation. Jul 2023 – Dec 2023 New York City, NY

●Developed a custom deep learning model to classify the sentiment of financial messages (Stocktwits), successfully generating a signal of public sentiment for various ticker symbols.

●Built a statistical risk model using Principal Component Analysis (PCA) and developed 5 proprietary alpha factors to aid in portfolio construction.

●Accelerated the backtesting engine's execution speed by leveraging NumPy vectorization and Python multiprocessing, enabling the rapid iteration of massive, multi-year historical datasets.

●Engineered a computationally efficient and realistic backtester using Barra data, which incorporated transaction costs and implemented performance attribution to identify major drivers of portfolio Profit-and-Loss (PnL).

●Implemented advanced portfolio optimization using quadratic programming to determine optimal weights, calculated tracking error against the index, and evaluated performance through factor-weighted returns, Sharpe ratio, and turnover analysis.

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